ZooTask_PreProcessing_11.07.23
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INSTALL NECESSARY PACKAGES:
# install.packages("erp.easy")
library(erp.easy)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(Hmisc)## Loading required package: lattice## Loading required package: survival## Loading required package: Formula## Loading required package: ggplot2##
## Attaching package: 'Hmisc'## The following objects are masked from 'package:dplyr':
##
## src, summarize## The following objects are masked from 'package:base':
##
## format.pval, unitslibrary(ggplot2, quietly = TRUE, warn.conflicts = FALSE)
library(stringr)
library(tidyr)
library(reshape2)##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smithsLOCATE FOLDERS:
# Locate the folder for the EEG output files (.txt) for old and new nets, replace the file location below with the one in your local device:
path_newnets <- "/Users/aysuerdemir/Desktop/R workspace/ERP_Zoo/CrossSectional/Mix/newnets/"
path_oldnets <- "/Users/aysuerdemir/Desktop/R workspace/ERP_Zoo/CrossSectional/Mix/oldnets/"
# Enter the number of participants in each group:
subs_new <- 70
subs_old <- 12LOAD DATA:
- Load the data from all subjects into a dataframe for each of the 4 conditions.
- Convert all variables into factors so that the grand average function can work properly.
- Do this separately for old nets and new nets since we will process them differently based on electrode locations (electrode numbers are different)
- Make sure all files are 250m/s sampling rate - downsample beforehand if needed. Code would not run if a child has higher sampling rate.
# Load data into dataframes for each condition separately (the exported .txt files appear separately for each condition):
neg_go <- load.data(path_newnets,"NegGo", subs_new, -100, 999)
neg_nogo <- load.data(path_newnets,"NegNoGo", subs_new, -100, 999)
neut_go <- load.data(path_newnets,"NeutGo", subs_new, -100, 999)
neut_nogo <- load.data(path_newnets,"NeutNoGo", subs_new, -100, 999)
# Combine all conditions together into a single dataframe:
combo_new <- rbind.data.frame(neg_go, neg_nogo, neut_go, neut_nogo)
combo_new <- as.data.frame(unclass(combo_new), stringsAsFactors=TRUE)
# Repeat for old nets:
neg_go_old <- load.data(path_oldnets,"NegGo", subs_old, -100, 999)
neg_nogo_old <- load.data(path_oldnets,"NegNoGo", subs_old, -100, 999)
neut_go_old <- load.data(path_oldnets,"NeutGo", subs_old, -100, 999)
neut_nogo_old <- load.data(path_oldnets,"NeutNoGo", subs_old, -100, 999)
combo_old <- rbind.data.frame(neg_go_old, neg_nogo_old, neut_go_old, neut_nogo_old)
combo_old <- as.data.frame(unclass(combo_old),stringsAsFactors=TRUE)
head(combo_old)## Subject Stimulus Time V1 V2 V3 V4 V5
## 1 AN122116 NegGo -100 -1.602166 -0.975723 -0.816704 -1.601339 -0.178200
## 2 AN122116 NegGo -96 -1.381717 -0.857681 -0.553844 -0.892159 0.475065
## 3 AN122116 NegGo -92 -0.916006 -0.567245 -0.245930 -0.231857 1.066748
## 4 AN122116 NegGo -88 -0.271272 -0.088409 0.125656 0.340374 1.469912
## 5 AN122116 NegGo -84 0.449695 0.554581 0.589043 0.840698 1.634542
## 6 AN122116 NegGo -80 1.147286 1.300797 1.164803 1.323421 1.615967
## V6 V7 V8 V9 V10 V11 V12
## 1 -0.331926 -0.151961 -0.347968 -0.576614 -1.093344 -1.390728 -1.446274
## 2 0.510233 0.606494 -0.153863 -0.260136 -0.415079 -0.283929 -0.484142
## 3 1.314797 1.333362 0.159202 0.034664 0.071394 0.791458 0.575123
## 4 1.898442 1.850423 0.551349 0.315926 0.373709 1.671227 1.558418
## 5 2.160961 2.056113 0.989324 0.634567 0.603586 2.271284 2.303516
## 6 2.138047 1.978029 1.457013 1.057877 0.921005 2.612692 2.723587
## V13 V14 V15 V16 V17 V18 V19 V20
## 1 0.154436 -0.183043 -0.986050 -1.668789 0.050217 -2.640511 -3.116714 -2.074911
## 2 0.945122 0.372879 -0.317448 -0.819794 0.983765 -1.523602 -1.794190 -1.187153
## 3 1.697449 0.793301 0.149136 0.003761 1.976685 -0.314388 -0.273128 -0.085423
## 4 2.217609 1.043167 0.441242 0.719955 2.786621 0.844208 1.214652 1.044960
## 5 2.406643 1.174946 0.670528 1.307877 3.234940 1.815136 2.451749 1.997532
## 6 2.313116 1.303911 0.981770 1.808807 3.283058 2.517334 3.306489 2.624617
## V21 V22 V23 V24 V25 V26 V27
## 1 -1.612476 -3.177330 -4.650845 -2.473480 -1.961811 -4.129242 -2.788494
## 2 -0.844498 -1.744621 -3.119498 -1.602276 -1.253998 -2.740967 -1.987232
## 3 0.098137 -0.120888 -1.237389 -0.474751 -0.290592 -1.055275 -0.902645
## 4 1.032101 1.405754 0.652441 0.778458 0.766792 0.610847 0.338024
## 5 1.764500 2.557701 2.195314 1.949837 1.698437 1.928261 1.515244
## 6 2.181705 3.180214 3.163316 2.833687 2.327084 2.689941 2.407352
## V28 V29 V30 V31 V32 V33 V34 V35
## 1 -1.600498 0.267609 1.704092 1.655323 1.792316 -1.247337 -1.565639 0.009226
## 2 -0.895760 0.061537 1.575657 1.587364 1.779469 -0.718452 -1.027107 -0.212204
## 3 0.037254 0.093109 1.446121 1.501526 1.689861 0.020538 -0.316589 -0.194126
## 4 1.056332 0.293676 1.282846 1.354988 1.483730 0.877956 0.484066 0.008957
## 5 1.947704 0.504688 1.051548 1.120262 1.165264 1.686760 1.225574 0.244831
## 6 2.520092 0.555014 0.739815 0.802414 0.785526 2.267891 1.750835 0.334261
## V36 V37 V38 V39 V40 V41 V42 V43
## 1 1.743858 2.122482 1.753445 0.243980 -0.360420 2.080679 2.637558 2.488483
## 2 1.622190 1.938988 1.444054 0.475021 -0.622421 1.851799 2.304211 1.770252
## 3 1.530930 1.721893 1.114063 0.813866 -0.621868 1.605459 1.966421 1.092953
## 4 1.412546 1.461838 0.791637 1.169640 -0.392212 1.336784 1.650398 0.551357
## 5 1.209280 1.153973 0.497448 1.426853 -0.084667 1.030151 1.340042 0.177834
## 6 0.894976 0.806694 0.245754 1.489670 0.100655 0.676033 0.992119 -0.057133
## V44 V45 V46 V47 V48 V49 V50
## 1 -0.478058 -0.116197 0.471390 0.894775 1.085449 -0.986816 -0.568535
## 2 -0.332985 -0.481936 -0.240822 0.001236 0.034695 -1.011177 -1.498154
## 3 -0.127296 -0.522299 -0.512642 -0.864632 -0.893692 -0.927132 -2.277191
## 4 0.048119 -0.285610 -0.346947 -1.536613 -1.526505 -0.777245 -2.780609
## 5 0.102623 0.035172 0.028740 -1.958992 -1.841530 -0.652672 -3.026688
## 6 -0.007149 0.196474 0.274559 -2.186066 -1.952347 -0.638664 -3.137680
## V51 V52 V53 V54 V55 V56 V57 V58
## 1 1.070000 1.878197 2.666451 1.737392 1.327618 1.859970 1.614403 2.925186
## 2 -0.160545 0.618912 1.864506 1.494488 1.545944 0.444145 0.366844 1.426971
## 3 -1.294258 -0.504265 1.024616 1.240417 1.603912 -0.640739 -0.757164 -0.111763
## 4 -2.129997 -1.287273 0.292511 0.979656 1.427685 -1.120575 -1.491501 -1.395485
## 5 -2.613663 -1.676357 -0.227856 0.723427 1.046879 -1.017914 -1.773453 -2.266602
## 6 -2.831451 -1.767654 -0.507278 0.491032 0.583645 -0.625381 -1.747795 -2.740128
## V59 V60 V61 V62 V63 V64 V65
## 1 2.046391 1.169464 1.071705 1.248970 1.905968 1.640926 0.002188
## 2 0.602528 0.107323 0.288998 1.150200 0.342133 0.277851 -0.932522
## 3 -0.905182 -0.946693 -0.552670 0.810631 -1.086975 -1.066990 -1.984392
## 4 -2.166298 -1.769859 -1.264359 0.314499 -2.020744 -2.095554 -2.859217
## 5 -2.966969 -2.223263 -1.700798 -0.188576 -2.319824 -2.660908 -3.353342
## 6 -3.268372 -2.299583 -1.814854 -0.550926 -2.108795 -2.801708 -3.424402
## V66 V67 V68 V69 V70 V71 V72
## 1 -0.476954 1.102089 0.719635 1.112701 -0.058893 -1.529216 -0.755066
## 2 -1.551037 0.714247 0.691925 -0.130219 -0.911140 -2.128438 -1.079086
## 3 -2.686024 0.118654 0.379486 -1.340378 -1.866013 -2.868699 -1.558258
## 4 -3.593519 -0.555941 -0.125336 -2.261200 -2.673973 -3.511125 -2.041573
## 5 -4.072910 -1.152937 -0.655533 -2.744957 -3.165016 -3.872424 -2.387306
## 6 -4.085543 -1.551479 -1.043991 -2.798599 -3.303223 -3.889922 -2.522308
## V73 V74 V75 V76 V77 V78 V79 V80
## 1 0.441437 1.033527 0.788539 -0.928551 0.435883 1.081963 2.877953 1.601650
## 2 0.346336 0.178783 0.142629 -1.132133 0.487083 1.028404 2.960096 1.862282
## 3 0.022926 -0.656792 -0.560802 -1.508812 0.197792 0.684425 2.804230 1.968708
## 4 -0.425003 -1.350021 -1.180889 -1.935188 -0.334747 0.144081 2.413523 1.838276
## 5 -0.850845 -1.810688 -1.617779 -2.277023 -0.924843 -0.414665 1.895298 1.485500
## 6 -1.131081 -2.012646 -1.844580 -2.443001 -1.380531 -0.811607 1.405289 1.014156
## V81 V82 V83 V84 V85 V86 V87 V88
## 1 0.848020 0.460829 -1.231041 0.076736 0.138611 2.784137 3.245194 2.589694
## 2 1.362525 -0.121952 -1.526024 0.125122 0.144287 2.785977 3.160503 2.606918
## 3 1.802489 -0.749256 -1.918602 -0.192474 -0.095490 2.586747 2.883027 2.559120
## 4 2.045585 -1.354576 -2.313644 -0.775436 -0.506226 2.155099 2.388915 2.431592
## 5 2.060138 -1.855576 -2.615715 -1.429977 -0.948303 1.556672 1.757413 2.244977
## 6 1.915174 -2.186262 -2.768425 -1.953003 -1.282578 0.921325 1.127977 2.041994
## V89 V90 V91 V92 V93 V94 V95 V96
## 1 -3.432014 -4.049820 -0.531204 2.401985 1.829981 3.451154 -7.335152 -3.743881
## 2 -3.773735 -3.862244 -0.350449 2.178871 1.818653 3.347659 -7.251877 -3.261444
## 3 -4.165321 -3.830048 -0.507690 1.777306 1.638340 3.070870 -7.257523 -3.043457
## 4 -4.501930 -3.879913 -0.965775 1.195198 1.223476 2.590276 -7.266975 -3.139648
## 5 -4.653816 -3.891159 -1.573624 0.498116 0.631874 1.963420 -7.130493 -3.444870
## 6 -4.536186 -3.759476 -2.132874 -0.209702 0.001291 1.298814 -6.726074 -3.756775
## V97 V98 V99 V100 V101 V102 V103 V104
## 1 1.323845 1.687925 1.786144 -4.663246 -2.786003 0.974030 0.679365 1.707811
## 2 1.275421 1.668030 1.726355 -4.882811 -2.492363 0.976803 0.704914 1.570347
## 3 0.907425 1.538475 1.584257 -5.189646 -2.378700 1.009205 0.810230 1.400173
## 4 0.181274 1.197460 1.261652 -5.422186 -2.516228 0.916962 0.854427 1.136353
## 5 -0.772461 0.651726 0.757948 -5.394495 -2.855194 0.627228 0.746681 0.772690
## 6 -1.715271 -0.001589 0.154560 -5.005291 -3.257599 0.161217 0.467112 0.346331
## V105 V106 V107 V108 V109 V110 V111 V112
## 1 3.157804 0.831240 -0.703478 -1.796181 -0.954175 1.336458 1.670211 1.949009
## 2 3.067249 1.364780 -0.046059 -2.326027 -0.743787 1.344398 1.535836 2.193771
## 3 2.822426 1.789238 0.593539 -2.794525 -0.242855 1.456902 1.459781 2.292099
## 4 2.376984 1.999977 1.056548 -2.981224 0.310246 1.582771 1.452214 2.270863
## 5 1.775845 1.987045 1.255971 -2.768326 0.687672 1.625034 1.492644 2.191158
## 6 1.124949 1.833754 1.224303 -2.219368 0.758452 1.505865 1.527507 2.118137
## V113 V114 V115 V116 V117 V118 V119 V120
## 1 -0.539551 -1.150284 -1.752474 -1.822952 1.795980 1.812792 -0.461781 -0.497742
## 2 0.007809 -1.588699 -1.885380 -1.500015 1.551477 1.941059 -0.014542 -0.606674
## 3 0.575939 -1.906067 -1.803888 -0.736660 1.388708 1.938408 0.414474 -0.748016
## 4 1.070267 -1.970215 -1.570978 0.139423 1.359306 1.884426 0.785519 -0.875206
## 5 1.428329 -1.725597 -1.263821 0.797108 1.457943 1.859196 1.088673 -0.915016
## 6 1.653008 -1.227242 -0.942726 1.035713 1.612221 1.904932 1.350513 -0.806335
## V121 V122 V123 V124 V125 V126 V127
## 1 -2.506559 -1.355415 1.636124 -0.138519 -0.575409 -0.063583 -1.093063
## 2 -2.282681 -1.201950 1.676567 0.323805 -0.402016 0.300964 -0.635666
## 3 -1.849167 -0.738174 1.668022 0.681597 -0.205864 0.443298 -0.282318
## 4 -1.320885 -0.027748 1.768433 0.942221 -0.033119 0.364227 -0.125992
## 5 -0.809322 0.793083 2.099190 1.165342 0.105431 0.162506 -0.223267
## 6 -0.376049 1.563683 2.669037 1.429461 0.232117 -0.026382 -0.554886
## V128 V129
## 1 -0.922371 0.064854
## 2 -0.654320 0.651667
## 3 -0.289268 1.148424
## 4 0.054832 1.419500
## 5 0.246422 1.427877
## 6 0.202545 1.258244SPECIFY THE ELECTRODE NUMBERS FOR P2, N2 and P3:
FCz = frontrocentral - midline FC4 = frontrocentral - right FC3 = frontrocentral - left
Pz = parietal - midline P4 = parietal - right P3 = parietal - left
# new nets:
FCz_newnets <- c("V18", "V16", "V10", "V19", "V11", "V4", "V12", "V5", "V6")
FC4_newnets <- c("V3","V123","V124", "V117", "V118")
FC3_newnets <- c("V23","V27","V24", "V28", "V20")
Pz_newnets <- c("V54","V79","V61", "V62","V78", "V67", "V72", "V77")
P4_newnets <- c("V85", "V86", "V91" , "V92" ,"V97", "V98")
P3_newnets <- c("V51", "V52","V53", "V59", "V60", "V47")
# repeat for old nets
FCz_oldnets <- c("V19","V16", "V10", "V20","V11", "V4","V12","V5","V6")
FC4_oldnets <- c("V3","V123","V119", "V123","V118")
FC3_oldnets <- c("V24","V25","V21", "V28","V29")
Pz_oldnets <- c( "V54","V62","V80", "V61","V68","V79", "V67", "V73","V78")
P4_oldnets <- c("V87","V99", "V86", "V93", "V98", "V92")
P3_oldnets <- c("V53","V48", "V52", "V60", "V51", "V59") CHECK OUT INDIVIDUAL WAVEFORMS:
USING MOSAIC YOU CAN CREATE AN AVERAGE WAVEFORM FOR EACH PARTICIPANT IN EACH GROUP & NET LOCATION:
#Create average waveform plots for each subject in a single, multiplot window
mosaic(combo_new, FCz_newnets, cols = 3, rows = 2)
mosaic(combo_new, Pz_newnets, cols = 3, rows = 2)
mosaic(combo_old, FCz_oldnets, cols = 3, rows = 2)
mosaic(combo_old, Pz_oldnets, cols = 3, rows = 2)
CHECK OUT GRADN AVERAGES USING GRANDAVERAGE() FUNCTION
# grand average plots the grand average waveform for each condition present in the dataframe you provide.
# A color-coded and labeled legend is generated with the plot for ease of identification of each condition.
grandaverage(combo_new, FCz_newnets)
## [[1]]
## [1] 0.620642811 0.536617575 0.444544065 0.350994646 0.266199886
## [6] 0.200362103 0.159344825 0.141578127 0.137688784 0.133321806
## [11] 0.114262289 0.071871425 0.006608219 -0.071913768 -0.148551006
## [16] -0.208585024 -0.243585932 -0.254755410 -0.252279222 -0.250926089
## [21] -0.263731908 -0.296445421 -0.345153843 -0.398165552 -0.441318179
## [26] -0.464391487 -0.465775446 -0.453271767 -0.440621606 -0.441222268
## [31] -0.461885316 -0.499533657 -0.542552297 -0.576504476 -0.591979389
## [36] -0.591323862 -0.591313506 -0.620447854 -0.711652568 -0.893069805
## [41] -1.180311856 -1.572877178 -2.055664638 -2.604468375 -3.192922510
## [46] -3.798023925 -4.402404894 -4.993173562 -5.558820152 -6.086440603
## [51] -6.561104519 -6.967905411 -7.295622073 -7.539992221 -7.704640519
## [56] -7.798871740 -7.833190278 -7.814645657 -7.744340052 -7.618513568
## [61] -7.432897879 -7.188322881 -6.894842281 -6.572198583 -6.246034465
## [66] -5.941345208 -5.675957340 -5.456888241 -5.281102714 -5.140175057
## [71] -5.026550217 -4.938377302 -4.880707729 -4.862646275 -4.892063651
## [76] -4.970605876 -5.091498673 -5.241153616 -5.403658419 -5.565842735
## [81] -5.720392548 -5.865668711 -6.002776141 -6.131839437 -6.249848992
## [86] -6.351495602 -6.432624452 -6.494297752 -6.544852703 -6.598276286
## [91] -6.669143919 -6.766328910 -6.888638887 -7.024823703 -7.158454371
## [96] -7.275668254 -7.372218148 -7.456389224 -7.546206673 -7.662025486
## [101] -7.817859662 -8.015500933 -8.244169902 -8.485896722 -8.723999467
## [106] -8.950635135 -9.169843189 -9.394693083 -9.639992148 -9.914107987
## [111] -10.213812386 -10.524581756 -10.826113875 -11.100357794 -11.338289051
## [116] -11.542420787 -11.724100097 -11.897144268 -12.070977506 -12.246448410
## [121] -12.416056522 -12.568107044 -12.692575548 -12.785854414 -12.852320311
## [126] -12.902358719 -12.948091044 -12.999003687 -13.059376756 -13.128298608
## [131] -13.201722149 -13.275173059 -13.345647579 -13.411988989 -13.474004687
## [136] -13.531213200 -13.582276629 -13.625495843 -13.660041352 -13.686964744
## [141] -13.709166619 -13.729966214 -13.750733938 -13.768760095 -13.776529825
## [146] -13.763092621 -13.717269297 -13.631586714 -13.505419686 -13.346011038
## [151] -13.166797429 -12.983486394 -12.809222241 -12.650565095 -12.505760670
## [156] -12.365969744 -12.219076917 -12.054734722 -11.868827992 -11.665637757
## [161] -11.456812570 -11.257484165 -11.080897552 -10.933608713 -10.813162124
## [166] -10.709218367 -10.607712181 -10.496351821 -10.369130303 -10.227962151
## [171] -10.080790302 -9.937100341 -9.802980444 -9.678116824 -9.556167422
## [176] -9.428305283 -9.288155473 -9.135586646 -8.977307168 -8.823835054
## [181] -8.684220168 -8.561142267 -8.449006617 -8.336189567 -8.210597441
## [186] -8.065901240 -7.905425392 -7.741607371 -7.591071610 -7.467489779
## [191] -7.375527097 -7.308701313 -7.252079913 -7.188528022 -7.105538705
## [196] -6.999535622 -6.875889127 -6.745013132 -6.616821775 -6.496372330
## [201] -6.382716562 -6.271101640 -6.156886086 -6.038749667 -5.919273573
## [206] -5.802640430 -5.690866329 -5.580972114 -5.464970716 -5.333017414
## [211] -5.178267486 -5.000982925 -4.809586359 -4.617918603 -4.439903879
## [216] -4.284211711 -4.151457589 -4.035254938 -3.926421279 -3.818064598
## [221] -3.708877773 -3.602987486 -3.506558159 -3.423149338 -3.350462971
## [226] -3.280342871 -3.202145506 -3.107746414 -2.995551365 -2.871336103
## [231] -2.745427722 -2.627672197 -2.522849259 -2.428948770 -2.339182468
## [236] -2.246637657 -2.148981594 -2.050487238 -1.960032963 -1.885852238
## [241] -1.829675051 -1.783413081 -1.730497565 -1.651763146 -1.533491168
## [246] -1.373989789 -1.185531351 -0.990499598 -0.813160332 -0.670513719
## [251] -0.566123917 -0.489496494 -0.421195252 -0.341365478 -0.237943487
## [256] -0.111140083 0.027524446 0.160845565 0.273552573 0.358575390
## [261] 0.419431648 0.468027573 0.519205959 0.584632738 0.668611063
## [266] 0.767346159 0.871537748 0.970800175 1.057822444 1.130575137
## [271] 1.191955527 1.247481986 1.302391046 1.359533219 1.418875954
##
## [[2]]
## [1] 0.436058541 0.376215329 0.333848603 0.307213040 0.289065279
## [6] 0.269118489 0.238573033 0.194614795 0.142356297 0.092857041
## [11] 0.057887695 0.043909514 0.048080227 0.058330903 0.057709537
## [16] 0.031214276 -0.027980546 -0.115725835 -0.218640260 -0.319176194
## [21] -0.401614670 -0.456409930 -0.481379322 -0.479887684 -0.457787483
## [26] -0.421296598 -0.376911489 -0.332845621 -0.300179533 -0.291957190
## [31] -0.319608817 -0.387764630 -0.490030433 -0.608727135 -0.720089624
## [36] -0.803731017 -0.852850603 -0.880802297 -0.920670257 -1.017146084
## [41] -1.213117702 -1.535906887 -1.988402894 -2.548303033 -3.175340465
## [46] -3.823386643 -4.452754990 -5.038331048 -5.571235984 -6.054598897
## [51] -6.496327868 -6.902328178 -7.272552413 -7.600510368 -7.875365038
## [56] -8.084913546 -8.217921960 -8.265280516 -8.220708724 -8.081896457
## [61] -7.852356408 -7.543268321 -7.174052368 -6.770450506 -6.359785800
## [66] -5.964325887 -5.595028438 -5.248338608 -4.907751230 -4.550055411
## [71] -4.154447695 -3.711644654 -3.229828356 -2.735120854 -2.266196751
## [76] -1.864577495 -1.563544343 -1.378910754 -1.304451756 -1.313573608
## [81] -1.366979143 -1.424173184 -1.455246449 -1.449352238 -1.417007894
## [86] -1.385233611 -1.386971808 -1.448551003 -1.579949383 -1.771634059
## [91] -1.999274127 -2.234506094 -2.457436338 -2.665539067 -2.874880146
## [96] -3.113101917 -3.407256257 -3.771912178 -4.202744973 -4.678397995
## [101] -5.169674113 -5.651647494 -6.112806240 -6.556916203 -6.997082916
## [106] -7.445217311 -7.902352529 -8.354712494 -8.777716676 -9.146053117
## [111] -9.444743044 -9.675600906 -9.855907765 -10.010021638 -10.158036265
## [116] -10.306932029 -10.448518162 -10.565006073 -10.639274954 -10.664571389
## [121] -10.648920383 -10.612472125 -10.579340163 -10.568040422 -10.585080537
## [126] -10.624604265 -10.673712830 -10.720326114 -10.759490989 -10.795265008
## [131] -10.837759860 -10.897175279 -10.977876352 -11.075370268 -11.177338790
## [136] -11.267801311 -11.332168954 -11.361213697 -11.352883221 -11.311937140
## [141] -11.248065287 -11.173333016 -11.099488540 -11.035252000 -10.983664449
## [146] -10.940262713 -10.893310316 -10.826929968 -10.726811114 -10.586696063
## [151] -10.412896805 -10.224208940 -10.046169505 -9.901175338 -9.798539095
## [156] -9.729140905 -9.667544767 -9.581379798 -9.444363803 -9.247472470
## [161] -9.003136027 -8.740388979 -8.493186522 -8.287210817 -8.131060348
## [166] -8.015477884 -7.920446098 -7.826368549 -7.723753305 -7.616743963
## [171] -7.519300630 -7.446670714 -7.406822675 -7.396181348 -7.401443662
## [176] -7.406070432 -7.397652706 -7.372129821 -7.332820402 -7.285541054
## [181] -7.233179232 -7.173151263 -7.099340368 -7.007434216 -6.900279495
## [186] -6.789550375 -6.691870857 -6.620854232 -6.578978211 -6.553783478
## [191] -6.520896086 -6.453168390 -6.332189500 -6.156798524 -5.944415762
## [196] -5.724420921 -5.526502765 -5.369253586 -5.253865398 -5.165463752
## [201] -5.081129684 -4.980645154 -4.854913354 -4.708422268 -4.555314030
## [206] -4.411444006 -4.286357290 -4.178841695 -4.077764149 -3.967502841
## [211] -3.835122568 -3.675947068 -3.495385025 -3.306642656 -3.125617913
## [216] -2.965065781 -2.830280827 -2.717685070 -2.616458365 -2.512256868
## [221] -2.391719533 -2.246577924 -2.076199613 -1.887805573 -1.694244249
## [226] -1.510018406 -1.346769033 -1.209619202 -1.095801667 -0.996306644
## [231] -0.900161773 -0.799612359 -0.693938746 -0.590137178 -0.500021997
## [236] -0.434870016 -0.399781929 -0.390611784 -0.395138295 -0.398120644
## [241] -0.387911173 -0.361588694 -0.326234148 -0.295596703 -0.283500505
## [246] -0.297077040 -0.333073438 -0.378980665 -0.418315940 -0.437471735
## [251] -0.430977887 -0.402800644 -0.363197749 -0.322819275 -0.287224760
## [256] -0.254623500 -0.217808684 -0.169099833 -0.105687581 -0.032619543
## [261] 0.038286957 0.093226662 0.122469960 0.124618127 0.106919840
## [266] 0.081585294 0.059810730 0.046364792 0.037423619 0.022842354
## [271] -0.008191408 -0.060906981 -0.131154203 -0.205829552 -0.267382194
##
## [[3]]
## [1] 0.44000211 0.35993513 0.29113869 0.24170809 0.21264901
## [6] 0.19747459 0.18552431 0.16723828 0.13858045 0.10230704
## [11] 0.06544650 0.03444344 0.01071746 -0.01069943 -0.03847425
## [16] -0.07958578 -0.13427998 -0.19440565 -0.24651410 -0.27850545
## [21] -0.28654922 -0.27847246 -0.27136453 -0.28406251 -0.32804125
## [26] -0.40124354 -0.48818566 -0.56661233 -0.61767691 -0.63469492
## [31] -0.62606520 -0.61083301 -0.60921339 -0.63307183 -0.68149437
## [36] -0.74403200 -0.81024305 -0.88072961 -0.97365716 -1.12268559
## [41] -1.36633329 -1.73310147 -2.22889926 -2.83245472 -3.50086093
## [46] -4.18285664 -4.83401028 -5.42736654 -5.95553382 -6.42443809
## [51] -6.84268020 -7.21211525 -7.52400504 -7.76205591 -7.91008909
## [56] -7.95996853 -7.91573041 -7.79229872 -7.61006574 -7.38854991
## [61] -7.14240891 -6.88138126 -6.61331341 -6.34767920 -6.09690451
## [66] -5.87438529 -5.69025717 -5.54747048 -5.44069752 -5.35902557
## [71] -5.29137587 -5.23188977 -5.18249751 -5.15131498 -5.14785267
## [76] -5.17762309 -5.23899975 -5.32388208 -5.42161748 -5.52386061
## [81] -5.62756126 -5.73441700 -5.84727862 -5.96577387 -6.08389384
## [86] -6.19122865 -6.27749881 -6.33810985 -6.37771473 -6.40965061
## [91] -6.45127695 -6.51744098 -6.61521839 -6.74240482 -6.89029897
## [96] -7.04916398 -7.21336428 -7.38347317 -7.56440747 -7.76091349
## [101] -7.97308425 -8.19458998 -8.41470936 -8.62324465 -8.81569370
## [106] -8.99588929 -9.17452459 -9.36421331 -9.57346509 -9.80250193
## [111] -10.04285193 -10.28075123 -10.50259013 -10.69970058 -10.87035258
## [116] -11.01837162 -11.14961804 -11.26844520 -11.37595114 -11.47067313
## [121] -11.55097023 -11.61736265 -11.67329832 -11.72383913 -11.77314921
## [126] -11.82234258 -11.86904870 -11.90900906 -11.93883844 -11.95836020
## [131] -11.97105244 -11.98222022 -11.99586285 -12.01202572 -12.02615180
## [136] -12.03094963 -12.01992370 -11.99077090 -11.94679782 -11.89549376
## [141] -11.84492494 -11.79975224 -11.75887951 -11.71590746 -11.66212524
## [146] -11.59054517 -11.49892717 -11.39027732 -11.27065541 -11.14558322
## [151] -11.01697617 -10.88222836 -10.73590728 -10.57319772 -10.39322541
## [156] -10.20048341 -10.00343728 -9.81094787 -9.62819395 -9.45407209
## [161] -9.28140998 -9.10001842 -8.90135776 -8.68280146 -8.44961832
## [166] -8.21382658 -7.99046174 -7.79269706 -7.62769223 -7.49462844
## [171] -7.38550328 -7.28821012 -7.19059085 -7.08399277 -6.96529179
## [176] -6.83690618 -6.70507739 -6.57714601 -6.45886193 -6.35256606
## [181] -6.25670460 -6.16669258 -6.07673530 -5.98195712 -5.88007054
## [186] -5.77191415 -5.66069248 -5.55025295 -5.44307045 -5.33880188
## [191] -5.23409494 -5.12394408 -5.00410938 -4.87359008 -4.73599427
## [196] -4.59905027 -4.47220562 -4.36306801 -4.27404604 -4.20070056
## [201] -4.13265423 -4.05690312 -3.96236312 -3.84397439 -3.70471303
## [206] -3.55464844 -3.40729356 -3.27467579 -3.16307781 -3.07107260
## [211] -2.99056525 -2.91035479 -2.82073115 -2.71717868 -2.60168053
## [216] -2.48120726 -2.36418382 -2.25655200 -2.15914658 -2.06745532
## [221] -1.97382282 -1.87096520 -1.75513347 -1.62743000 -1.49278629
## [226] -1.35719722 -1.22464459 -1.09523380 -0.96561845 -0.83150785
## [231] -0.69098072 -0.54683605 -0.40662043 -0.28005757 -0.17487525
## [236] -0.09289432 -0.02836163 0.03044279 0.09677594 0.18005066
## [241] 0.28188401 0.39540485 0.50830137 0.60838052 0.68916116
## [246] 0.75291389 0.80965615 0.87248220 0.95130266 1.04778634
## [251] 1.15391353 1.25494939 1.33566080 1.38701021 1.41032375
## [256] 1.41700651 1.42392013 1.44633070 1.49150121 1.55573831
## [261] 1.62611967 1.68609471 1.72241724 1.73045151 1.71573272
## [266] 1.69135008 1.67253000 1.67101066 1.69160752 1.73220640
## [271] 1.78667474 1.84887969 1.91573509 1.98779890 2.06740503
##
## [[4]]
## [1] 0.39975740 0.34610585 0.30585477 0.27462875 0.24665815
## [6] 0.21522409 0.17391607 0.11879264 0.05083308 -0.02274529
## [11] -0.08962940 -0.13628564 -0.15334031 -0.14012060 -0.10602736
## [16] -0.06769487 -0.04283334 -0.04332207 -0.07074717 -0.11655280
## [21] -0.16668197 -0.20841091 -0.23594682 -0.25204857 -0.26509003
## [26] -0.28331381 -0.30958210 -0.33957628 -0.36453047 -0.37687871
## [31] -0.37557989 -0.36795865 -0.36683017 -0.38437738 -0.42626844
## [36] -0.48971565 -0.56717539 -0.65425335 -0.75771494 -0.89891985
## [41] -1.10979745 -1.42206293 -1.85382849 -2.39950832 -3.02798593
## [46] -3.69048535 -4.33530950 -4.92362484 -5.43992190 -5.89313012
## [51] -6.30831789 -6.71273990 -7.12211787 -7.53243649 -7.91986150
## [56] -8.24799647 -8.47911451 -8.58510314 -8.55474266 -8.39574124
## [61] -8.13196996 -7.79750719 -7.42928276 -7.05973780 -6.71070843
## [66] -6.38951086 -6.08814850 -5.78639449 -5.45855601 -5.08261080
## [71] -4.64905541 -4.16636213 -3.66072883 -3.16982247 -2.73251661
## [76] -2.37827852 -2.12009933 -1.95344322 -1.86109582 -1.82139180
## [81] -1.81614252 -1.83513586 -1.87599152 -1.94041553 -2.02952212
## [86] -2.14088587 -2.26876110 -2.40699951 -2.55263420 -2.70796473
## [91] -2.87991536 -3.07698998 -3.30545806 -3.56677999 -3.85752925
## [96] -4.17164938 -4.50362720 -4.85080652 -5.21366768 -5.59412169
## [101] -5.99291681 -6.40759335 -6.83210836 -7.25817645 -7.67740535
## [106] -8.08305151 -8.47061638 -8.83734550 -9.18134290 -9.50112186
## [111] -9.79595938 -10.06671708 -10.31624348 -10.54854322 -10.76678344
## [116] -10.97097636 -11.15674714 -11.31617168 -11.44070860 -11.52504416
## [121] -11.57001767 -11.58308063 -11.57585912 -11.55999764 -11.54333526
## [126] -11.52827906 -11.51309302 -11.49518294 -11.47435230 -11.45396116
## [131] -11.43908990 -11.43274909 -11.43240713 -11.42923103 -11.41110244
## [136] -11.36843534 -11.30000428 -11.21562623 -11.13374156 -11.07441971
## [141] -11.05070328 -11.06228021 -11.09464200 -11.12433948 -11.12825086
## [146] -11.09272376 -11.01840287 -10.91868167 -10.81266045 -10.71596868
## [151] -10.63383596 -10.55949355 -10.47837075 -10.37586066 -10.24473593
## [156] -10.08867414 -9.92034682 -9.75528264 -9.60455631 -9.46987288
## [161] -9.34336178 -9.21204239 -9.06476824 -8.89831413 -8.71984784
## [166] -8.54466852 -8.39018096 -8.26867130 -8.18186058 -8.11940733
## [171] -8.06194171 -7.98751592 -7.87906976 -7.73034187 -7.54824250
## [176] -7.35081757 -7.16146627 -7.00133189 -6.88244779 -6.80394013
## [181] -6.75266490 -6.70812293 -6.64994281 -6.56515049 -6.45235295
## [186] -6.32118560 -6.18718588 -6.06423307 -5.95784719 -5.86235012
## [191] -5.76320482 -5.64362600 -5.49236691 -5.30901737 -5.10435773
## [196] -4.89546790 -4.69782307 -4.51813030 -4.35135276 -4.18335549
## [201] -3.99792814 -3.78480250 -3.54493727 -3.29074356 -3.04125017
## [206] -2.81472325 -2.62227662 -2.46530654 -2.33734638 -2.22878079
## [211] -2.13159647 -2.04163473 -1.95754073 -1.87760447 -1.79694738
## [216] -1.70727623 -1.59984032 -1.47007372 -1.32133967 -1.16532389
## [221] -1.01826572 -0.89447466 -0.80005976 -0.72993368 -0.66966816
## [226] -0.60135945 -0.51088143 -0.39330455 -0.25423717 -0.10690676
## [231] 0.03335943 0.15461733 0.25151355 0.32545371 0.38247793
## [236] 0.43041361 0.47657827 0.52649525 0.58319849 0.64652886
## [241] 0.71218337 0.77134542 0.81193522 0.82215582 0.79556177
## [246] 0.73550514 0.65646679 0.58055353 0.52970196 0.51650787
## [251] 0.53786344 0.57498410 0.60057195 0.59048933 0.53485049
## [256] 0.44330520 0.34159485 0.26059133 0.22269600 0.23188612
## [261] 0.27213357 0.31498569 0.33269545 0.31062907 0.25301954
## [266] 0.17946133 0.11411364 0.07313828 0.05671626 0.04949986
## [271] 0.02910738 -0.02175542 -0.10574330 -0.20879695 -0.30659325grandaverage(combo_new, Pz_newnets)
## [[1]]
## [1] -0.3099443839 -0.2266575446 -0.1361012339 -0.0489629911 0.0221364804
## [6] 0.0672305714 0.0827642929 0.0729499071 0.0481206107 0.0209224089
## [11] 0.0019597554 -0.0033147982 0.0044920482 0.0200133500 0.0358125268
## [16] 0.0453403625 0.0450752821 0.0353497911 0.0199148893 0.0047505661
## [21] -0.0034956893 0.0008888982 0.0212130179 0.0573822500 0.1055426946
## [26] 0.1586123589 0.2077566018 0.2446064732 0.2635322286 0.2631041964
## [31] 0.2461665911 0.2185426893 0.1870965304 0.1581556661 0.1373081036
## [36] 0.1308742768 0.1484910321 0.2052787929 0.3220676250 0.5226898536
## [41] 0.8286928268 1.2528611214 1.7937338911 2.4330984732 3.1376100196
## [46] 3.8640298696 4.5664640786 5.2034231446 5.7429764018 6.1651240196
## [51] 6.4616325625 6.6342909786 6.6927560000 6.6526110125 6.5335715304
## [56] 6.3574370089 6.1455046018 5.9156707571 5.6798787268 5.4429639375
## [61] 5.2036081929 4.9573814875 4.7007810679 4.4347196911 4.1659852625
## [66] 3.9061275964 3.6681733536 3.4626162964 3.2944070679 3.1622866857
## [71] 3.0605648339 2.9824158518 2.9230351411 2.8813020375 2.8592024125
## [76] 2.8594843071 2.8828146786 2.9260038411 2.9822886518 3.0435659554
## [81] 3.1035475571 3.1603777018 3.2175206232 3.2824093321 3.3635054732
## [86] 3.4670964286 3.5953026714 3.7460285696 3.9147515036 4.0971422000
## [91] 4.2912840071 4.4983290339 4.7213185196 4.9627337054 5.2220790536
## [96] 5.4946040143 5.7718011036 6.0434876964 6.3006304500 6.5376458804
## [101] 6.7532459911 6.9495163964 7.1298256982 7.2965501857 7.4495814589
## [106] 7.5861817946 7.7021474036 7.7936583625 7.8588521893 7.8984999804
## [111] 7.9156014857 7.9143646750 7.8990829411 7.8735134339 7.8409436054
## [116] 7.8047954821 7.7691365500 7.7386361964 7.7177863071 7.7097698804
## [121] 7.7154566732 7.7330841946 7.7588832661 7.7885313714 7.8187910929
## [126] 7.8486554232 7.8795462286 7.9146177089 7.9575284286 8.0112182964
## [131] 8.0771711179 8.1553189732 8.2444347893 8.3425547982 8.4472019018
## [136] 8.5553750179 8.6636386321 8.7684958732 8.8671716375 8.9585311071
## [141] 9.0437065143 9.1258234643 9.2086652500 9.2946629232 9.3831685839
## [146] 9.4698706429 9.5478862143 9.6102832589 9.6531501036 9.6777814607
## [151] 9.6908425339 9.7021961554 9.7211991643 9.7529659893 9.7961811821
## [156] 9.8434516643 9.8841356893 9.9085136607 9.9114763732 9.8942458768
## [161] 9.8634807429 9.8283854911 9.7970924393 9.7739267482 9.7585956661
## [166] 9.7474966964 9.7362096107 9.7218853750 9.7043599304 9.6856592982
## [171] 9.6682216286 9.6528380589 9.6373342036 9.6167319179 9.5847664536
## [176] 9.5360527018 9.4679511786 9.3814386804 9.2806927696 9.1716483071
## [181] 9.0601477196 8.9503836464 8.8441704661 8.7410321857 8.6389091304
## [186] 8.5351069161 8.4271970286 8.3135837375 8.1937075268 8.0679780589
## [191] 7.9376546125 7.8046752946 7.6714867411 7.5408019732 7.4153283804
## [196] 7.2973176000 7.1879768036 7.0868262411 6.9913222750 6.8969681125
## [201] 6.7980768911 6.6891547429 6.5666354679 6.4303907607 6.2843001554
## [206] 6.1354274518 5.9918743786 5.8599419411 5.7415677446 5.6331392000
## [211] 5.5263439804 5.4110317857 5.2790553518 5.1276571875 4.9610621464
## [216] 4.7895740589 4.6263450964 4.4829830214 4.3655586304 4.2725393411
## [221] 4.1952358214 4.1204868893 4.0345039143 3.9266202179 3.7917600661
## [226] 3.6310877125 3.4509692536 3.2608880089 3.0710953214 2.8905087482
## [231] 2.7252298446 2.5778479018 2.4475763250 2.3310521821 2.2236316982
## [236] 2.1209017071 2.0199875786 1.9200823446 1.8219785804 1.7267094875
## [241] 1.6339851946 1.5411212232 1.4431598375 1.3343263714 1.2103739232
## [246] 1.0707233518 0.9193428750 0.7637797732 0.6127107000 0.4729911429
## [251] 0.3475032571 0.2347631536 0.1304685857 0.0301592875 -0.0683937036
## [256] -0.1642411107 -0.2541274661 -0.3346202054 -0.4043237446 -0.4649431250
## [261] -0.5206156857 -0.5758182911 -0.6331338714 -0.6920825089 -0.7497409214
## [266] -0.8027424339 -0.8496318232 -0.8922304000 -0.9352157518 -0.9839995107
## [271] -1.0421069625 -1.1094614393 -1.1825446482 -1.2562443500 -1.3263973000
##
## [[2]]
## [1] -0.2805428929 -0.1739377911 -0.0515514018 0.0639560839 0.1501216250
## [6] 0.1926871911 0.1900000143 0.1520652804 0.0953621661 0.0364928250
## [11] -0.0126200518 -0.0464592411 -0.0646095125 -0.0687950679 -0.0606091518
## [16] -0.0414497214 -0.0142954804 0.0147523357 0.0369021500 0.0434377214
## [21] 0.0297888911 -0.0008455839 -0.0367483661 -0.0611149036 -0.0587020429
## [26] -0.0225428375 0.0426208518 0.1217895625 0.1954897393 0.2475448964
## [31] 0.2714512732 0.2722809946 0.2633036446 0.2595566196 0.2721868857
## [36] 0.3064775071 0.3644064464 0.4502945429 0.5764517161 0.7649109714
## [41] 1.0430748446 1.4342389429 1.9469708589 2.5677871214 3.2602549375
## [46] 3.9712925839 4.6428930161 5.2250607446 5.6853343518 6.0121133429
## [51] 6.2120458750 6.3036813125 6.3101020446 6.2529242661 6.1491112357
## [56] 6.0104970071 5.8445484768 5.6550398107 5.4426311036 5.2059761018
## [61] 4.9435812571 4.6560808107 4.3481995893 4.0295786804 3.7134131732
## [66] 3.4128386982 3.1365458304 2.8861600482 2.6569453125 2.4416831018
## [71] 2.2360843625 2.0435100786 1.8764342321 1.7532923804 1.6916886321
## [76] 1.7012232625 1.7793403161 1.9119965161 2.0787597161 2.2602369982
## [81] 2.4446440018 2.6303357286 2.8232418750 3.0309428554 3.2568519375
## [86] 3.4973637929 3.7431135607 3.9836728679 4.2133710446 4.4349459214
## [91] 4.6587779304 4.8979234714 5.1617091696 5.4509055768 5.7566339214
## [96] 6.0634646143 6.3555076768 6.6226293054 6.8636498268 7.0850602429
## [101] 7.2963788536 7.5046084554 7.7101486446 7.9057902643 8.0792875018
## [106] 8.2182889196 8.3151608071 8.3694888929 8.3876718321 8.3804967964
## [111] 8.3597644875 8.3351862982 8.3125351804 8.2936402054 8.2775290696
## [116] 8.2617347375 8.2433660857 8.2202287679 8.1920103518 8.1611037714
## [121] 8.1326909804 8.1141655750 8.1137179071 8.1380077893 8.1896314018
## [126] 8.2658285911 8.3593170893 8.4609237518 8.5628443643 8.6613813893
## [131] 8.7580260161 8.8580529054 8.9670094536 9.0869169143 9.2143361321
## [136] 9.3411918161 9.4579951268 9.5581316268 9.6415708107 9.7159361482
## [141] 9.7939909821 9.8883246196 10.0056964625 10.1433866946 10.2888970643
## [146] 10.4232450857 10.5270338268 10.5869870982 10.6002732089 10.5749090268
## [151] 10.5265067946 10.4728972946 10.4284960964 10.4003551643 10.3875038286
## [156] 10.3837352536 10.3821760661 10.3793574500 10.3771551250 10.3820417304
## [161] 10.4018370000 10.4412701786 10.4985467179 10.5650164393 10.6281968196
## [166] 10.6768246125 10.7057867214 10.7190208786 10.7287474482 10.7508312839
## [171] 10.7979586268 10.8738582321 10.9712019161 11.0740036393 11.1635655125
## [176] 11.2257497214 11.2564989661 11.2628868089 11.2590791464 11.2592404804
## [181] 11.2709366750 11.2918735196 11.3113060018 11.3155537679 11.2951547268
## [186] 11.2499469446 11.1893459536 11.1276753750 11.0771458179 11.0418658500
## [191] 11.0156887268 10.9850458554 10.9358112661 10.8607479321 10.7635649643
## [196] 10.6574119982 10.5587453786 10.4794926821 10.4211228536 10.3734565071
## [201] 10.3191293018 10.2417592179 10.1336148357 9.9989735679 9.8517887643
## [206] 9.7090331929 9.5827667214 9.4745578518 9.3750440446 9.2689973964
## [211] 9.1431600196 8.9927952304 8.8239279089 8.6506277250 8.4886774750
## [216] 8.3484631446 8.2304006625 8.1251897339 8.0183308214 7.8962655161
## [221] 7.7511491411 7.5825802929 7.3960598661 7.1992843179 6.9984102446
## [226] 6.7964875036 6.5946299500 6.3945373554 6.2002799089 6.0181615179
## [231] 5.8547086018 5.7136299375 5.5934391321 5.4874722929 5.3868760964
## [236] 5.2851001357 5.1813521304 5.0811515964 4.9937651464 4.9274332321
## [241] 4.8844266125 4.8584052821 4.8359013696 4.8014150893 4.7436528679
## [246] 4.6600258393 4.5576545214 4.4504668839 4.3533828429 4.2760248750
## [251] 4.2189053929 4.1736509196 4.1266874821 4.0644168714 3.9778481107
## [256] 3.8651225196 3.7310488321 3.5841230893 3.4328054321 3.2830010446
## [261] 3.1373539429 2.9959957661 2.8579851357 2.7228049125 2.5910490821
## [266] 2.4639263893 2.3421063250 2.2250563000 2.1113036750 1.9993440000
## [271] 1.8886176857 1.7803709786 1.6778430143 1.5852979411 1.5060724321
##
## [[3]]
## [1] -0.032319182 -0.008137191 0.012102234 0.026555957 0.033891579
## [6] 0.034094757 0.028349939 0.018242961 0.004876932 -0.011522088
## [11] -0.031238329 -0.054006864 -0.077880645 -0.098840243 -0.111672966
## [16] -0.111798620 -0.097144580 -0.069135659 -0.032290841 0.007548704
## [21] 0.045637484 0.080183657 0.112623889 0.146143991 0.183316361
## [26] 0.224041504 0.264817071 0.299736405 0.322808000 0.330462189
## [31] 0.322906986 0.303711804 0.278198050 0.251864554 0.230053916
## [36] 0.219355905 0.230310466 0.279880120 0.391702832 0.592871155
## [41] 0.907571721 1.349544075 1.915985552 2.585246955 3.319464725
## [46] 4.071454463 4.793490189 5.444983077 5.996874525 6.432274682
## [51] 6.744348884 6.933312977 7.004287625 6.966911652 6.836106030
## [56] 6.632393314 6.380319234 6.104781489 5.826342677 5.557354127
## [61] 5.300726832 5.052264468 4.805804389 4.558880634 4.316288932
## [66] 4.089912470 3.894896859 3.743845359 3.641537489 3.582600045
## [71] 3.553230411 3.536144439 3.516482621 3.486234193 3.445697937
## [76] 3.401795220 3.364340818 3.342159812 3.340807521 3.362458589
## [81] 3.407344164 3.475387209 3.567023755 3.682819329 3.822182604
## [86] 3.982148157 4.157265191 4.341016062 4.528161466 4.716834175
## [91] 4.909349934 5.111341823 5.329427320 5.568255225 5.828166250
## [96] 6.104437066 6.388251493 6.668768075 6.935443470 7.179940918
## [101] 7.397154846 7.585237846 7.744917396 7.878627650 7.989757705
## [106] 8.081970698 8.158551093 8.221896357 8.273283389 8.312873507
## [111] 8.339958575 8.353445164 8.352465986 8.336843470 8.307193602
## [116] 8.264853682 8.211889279 8.151257609 8.086953934 8.023955209
## [121] 7.967808621 7.923731659 7.895314720 7.883369500 7.885704412
## [126] 7.898121139 7.916335334 7.938030952 7.964216964 7.999110670
## [131] 8.048353104 8.116208518 8.203139350 8.304994666 8.414247118
## [136] 8.522750654 8.624880471 8.719601671 8.810365613 8.902731636
## [141] 9.000873002 9.104718954 9.209091582 9.305253595 9.384247988
## [146] 9.440644411 9.474926666 9.493356770 9.505427864 9.520136066
## [151] 9.542715111 9.573053566 9.606313573 9.635354925 9.653759139
## [156] 9.658049309 9.648259120 9.626991464 9.597710746 9.563106613
## [161] 9.524174912 9.480365612 9.430571184 9.374285759 9.312302396
## [166] 9.246823093 9.181129941 9.118962016 9.063717607 9.017634116
## [171] 8.981134405 8.952371041 8.927082784 8.898998929 8.861080848
## [176] 8.807424104 8.735159354 8.645575338 8.544002293 8.438282402
## [181] 8.336194896 8.242714186 8.158345352 8.079250339 7.999006762
## [186] 7.911148189 7.811462196 7.699110943 7.576173461 7.445928916
## [191] 7.310934207 7.172037534 7.028731141 6.880491116 6.728300332
## [196] 6.575487130 6.427243254 6.288858209 6.163518255 6.050888286
## [201] 5.947228614 5.846978420 5.745044209 5.638825752 5.529025154
## [206] 5.418820839 5.311813375 5.209955591 5.112528596 5.016576429
## [211] 4.918454052 4.815698384 4.708216523 4.598110466 4.488101850
## [216] 4.379478687 4.270786907 4.157977775 4.036009768 3.901192166
## [221] 3.753213220 3.595752489 3.435153943 3.277675748 3.126652212
## [226] 2.980934523 2.835321239 2.682928884 2.518660371 2.342306334
## [231] 2.159792773 1.981846400 1.820643795 1.685713216 1.580571461
## [236] 1.501339068 1.437923834 1.377343723 1.307879570 1.222425007
## [241] 1.119999321 1.005130941 0.885487202 0.768728868 0.659844854
## [246] 0.560004791 0.467141593 0.377725739 0.288814177 0.199616413
## [251] 0.111895305 0.029037850 -0.045736895 -0.111053196 -0.168129250
## [256] -0.220333029 -0.271748311 -0.325232459 -0.380811134 -0.435240748
## [261] -0.483056014 -0.518649495 -0.538492273 -0.542672980 -0.535178979
## [266] -0.522718571 -0.512524280 -0.510126714 -0.518115566 -0.536315211
## [271] -0.563113225 -0.597230443 -0.639044670 -0.690749373 -0.755031932
##
## [[4]]
## [1] -0.1350278875 -0.1247200500 -0.1336267214 -0.1571642661 -0.1829112679
## [6] -0.1940614321 -0.1758355482 -0.1221050321 -0.0393350875 0.0541166679
## [11] 0.1337322143 0.1778116518 0.1759301732 0.1329262339 0.0667108571
## [16] 0.0011070571 -0.0430903357 -0.0551926839 -0.0369270036 0.0008484304
## [21] 0.0449951964 0.0866309161 0.1240000732 0.1608049429 0.2015021107
## [26] 0.2463547179 0.2890897500 0.3189020946 0.3259907411 0.3074511196
## [31] 0.2699810071 0.2279785018 0.1981357750 0.1933600500 0.2192461714
## [36] 0.2753702714 0.3611973661 0.4833389732 0.6596071589 0.9168725464
## [41] 1.2828267250 1.7746741268 2.3892775679 3.0992423393 3.8572541429
## [46] 4.6072600750 5.2978142964 5.8923661036 6.3732954607 6.7393064768
## [51] 6.9983713946 7.1599975054 7.2305838875 7.2134506929 7.1122073304
## [56] 6.9344097536 6.6930879643 6.4053239000 6.0886450482 5.7570866161
## [61] 5.4192323393 5.0794730679 4.7415066714 4.4115833536 4.0994827196
## [66] 3.8167251375 3.5728779161 3.3717057982 3.2092519250 3.0752859018
## [71] 2.9575909036 2.8468556964 2.7398475054 2.6398864589 2.5549216357
## [76] 2.4942925500 2.4657056286 2.4738613536 2.5209700446 2.6079789286
## [81] 2.7349677339 2.9004321786 3.1000417018 3.3259403286 3.5675148286
## [86] 3.8137888911 4.0570830732 4.2961541696 4.5367873893 4.7893411214
## [91] 5.0644094732 5.3684645304 5.7011907589 6.0555707643 6.4207028161
## [96] 6.7859218268 7.1438368821 7.4908562929 7.8255216768 8.1460262786
## [101] 8.4483427089 8.7258566839 8.9708242732 9.1768207000 9.3404659321
## [106] 9.4612332339 9.5397181321 9.5759180679 9.5688866696 9.5181936375
## [111] 9.4268204607 9.3041481482 9.1669635464 9.0368912304 8.9347338018
## [116] 8.8741444500 8.8573566964 8.8748045786 8.9089640893 8.9411605964
## [121] 8.9586171464 8.9585650893 8.9477312589 8.9381363714 8.9415676821
## [126] 8.9651705500 9.0098183357 9.0717443054 9.1462794411 9.2312160982
## [131] 9.3276504875 9.4381026036 9.5633119893 9.6995395536 9.8378342804
## [136] 9.9660610696 10.0731750589 10.1537157571 10.2101323000 10.2518201607
## [141] 10.2914808321 10.3404321089 10.4045317696 10.4823287911 10.5662773679
## [146] 10.6463564232 10.7141717286 10.7657447339 10.8022507321 10.8288288375
## [151] 10.8520823893 10.8771943982 10.9060007500 10.9368475429 10.9658657821
## [156] 10.9887769268 11.0025580982 11.0066900357 11.0035258643 10.9973869339
## [161] 10.9929696196 10.9938705054 11.0016877286 11.0156694054 11.0330365321
## [166] 11.0501502750 11.0640718429 11.0736491589 11.0795767571 11.0837102089
## [171] 11.0879899714 11.0932177946 11.0981754857 11.0997685482 11.0943729107
## [176] 11.0795749804 11.0552889964 11.0238597696 10.9891856482 10.9550044821
## [181] 10.9228612089 10.8908805429 10.8542466018 10.8072239000 10.7456470304
## [186] 10.6689394982 10.5809686643 10.4892086196 10.4021422839 10.3258803893
## [191] 10.2616498839 10.2052125036 10.1482815214 10.0813418464 9.9971224071
## [196] 9.8934490482 9.7740786804 9.6470268250 9.5212863446 9.4033358714
## [201] 9.2945570768 9.1903537661 9.0814561714 8.9571353714 8.8088948661
## [206] 8.6331528339 8.4322611518 8.2139614000 7.9896843679 7.7720164661
## [211] 7.5722011714 7.3983398036 7.2542825482 7.1387695036 7.0448681304
## [216] 6.9605243357 6.8706333768 6.7604821446 6.6199478839 6.4474254714
## [221] 6.2519040643 6.0515004286 5.8680752250 5.7196979071 5.6138409107
## [226] 5.5440193446 5.4915163732 5.4321398607 5.3457903482 5.2246937679
## [231] 5.0764241964 4.9202915125 4.7787022768 4.6671926500 4.5872456107
## [236] 4.5253607393 4.4591061875 4.3674199946 4.2401269679 4.0822984589
## [241] 3.9117026339 3.7506783500 3.6160487661 3.5118087696 3.4282026196
## [246] 3.3475819179 3.2538085607 3.1404290357 3.0139689732 2.8910607321
## [251] 2.7907558893 2.7256465500 2.6961331536 2.6905463661 2.6905670161
## [256] 2.6788066839 2.6451250036 2.5892844857 2.5192508196 2.4463310571
## [261] 2.3799767339 2.3248111071 2.2806125464 2.2440805268 2.2107361107
## [266] 2.1759616232 2.1350695554 2.0829235464 2.0142458786 1.9254640143
## [271] 1.8175424125 1.6977432607 1.5785866554 1.4738786625 1.3934069339# butterfly plots all individual waveforms for the condition specified by the stim argument(i.e.,a butterfly plot).
# The grandaverage waveform is also plotted,using a red line.
butterfly(combo_new,FCz_newnets, stim=1)
CODE BELOW GETS ALL THE MEASURES (N2, P2, P3) FROM OLD AND NEW NET DATA, COMBINE THEM TOGETHER AND IT SAVES THE DATA INTO A FINAL COMBO SPREADSHEET:
CHECK THE WINDOW RANGE FOR EACH ERP COMPONENT AND ADJUST AS NEEDED!
- The dependent measures we use are mean amplitude (microvolts) and latency (in ms) for statistical analysis.
- m.measures gives the mean amplitude for a specific window and its dt dev. along with its graph. specify lgnd = “n” (no legend) if you do not want the legend
- p.measures calculates local or simple peak amplitude and latency for each condition in the data frame. Use latency only from p.measures
- pol = The polarity of peaks to favor when multiple peaks are present. Entering “pos” will locate the most positive peak. Entering “neg” will locate the most negative peak. Entering “abs” will find the greatest deviation from 0, regardless of the polarity.
P2 between 180–280 ms after stimulus onset at frontal, frontocentral and central electrode sites.
N2 between 320,520 ms after stimulus onset at frontal, frontocentral and central electrode sites.
P3 between 450-750 ms after stimulus onset at parietal electrode sites.
# Get the mean Amplitude measures from the NEW net:
MeanAmp_P2_FCz_newnets <- (m.measures(combo_new, FCz_newnets, window=c(180,280)))
MeanAmp_P2_FC4_newnets <- (m.measures(combo_new, FC4_newnets, window=c(180,280)))
MeanAmp_P2_FC3_newnets <- (m.measures(combo_new, FC3_newnets, window=c(180,280))) 
MeanAmp_N2_FCz_newnets <- (m.measures(combo_new, FCz_newnets, window=c(320,520))) 
MeanAmp_N2_FC4_newnets <- (m.measures(combo_new, FC4_newnets, window=c(320,520)))
MeanAmp_N2_FC3_newnets<- (m.measures(combo_new, FC3_newnets, window=c(320,520))) 
MeanAmp_P3_Pz_newnets <- (m.measures(combo_new, Pz_newnets, window=c(450,750))) 
MeanAmp_P3_P4_newnets <- (m.measures(combo_new, P4_newnets, window=c(450,750))) 
MeanAmp_P3_P3_newnets <- (m.measures(combo_new, P3_newnets, window=c(450,750))) 
# We need to combine these but each one of these datasets use the same variable name - Mean Amplitude.
# Below is a function that will allow us to rename the variables in multiple datasets in a similar way:
rename_datasets_amplitude <- function(dataset_list, new_col_names){
for (i in 1:length(dataset_list)){
assign(dataset_list[i], rename(get(dataset_list[i]),
!!new_col_names[i] := "Mean Amplitude"), envir = .GlobalEnv)
}
}
datasets <- c("MeanAmp_P2_FCz_newnets", "MeanAmp_P2_FC4_newnets", "MeanAmp_P2_FC3_newnets",
"MeanAmp_N2_FCz_newnets", "MeanAmp_N2_FC4_newnets", "MeanAmp_N2_FC3_newnets",
"MeanAmp_P3_Pz_newnets", "MeanAmp_P3_P4_newnets", "MeanAmp_P3_P3_newnets")
new_column_names <- c("MeanAmp_P2_FCz", "MeanAmp_P2_FC4", "MeanAmp_P2_FC3",
"MeanAmp_N2_FCz", "MeanAmp_N2_FC4", "MeanAmp_N2_FC3",
"MeanAmp_P3_Pz", "MeanAmp_P3_P4", "MeanAmp_P3_P3")
rename_datasets_amplitude(datasets, new_column_names)
# load multiple datasets into the workspace
datasets_list <- mget(datasets)
# Using the `Reduce()` function to merge multiple data frames stored in `datasets_list` into a single data frame called `df_merge1`.
# It does this by merging each data frame in the list with the others, based on the columns "Subject" and "Trial Type".
merge_datasets_amplitude <- function(datasets_list) {
Reduce(function(x, y) {
x <- x[, !(names(x) %in% "Standard Dev")] # ignore Standard Dev column from the first dataframe, do not merge it
y <- y[, !(names(y) %in% "Standard Dev")] # ignore Standard Dev column from the next dataframe, do not merge it
merge(x, y, by=c("Subject", "Trial Type"))
}, datasets_list)
}
# Run the function
df_merge1 <- merge_datasets_amplitude(datasets_list) # use the function to merge the new data frames
# Get the Latency measures from the NEW net:
Latency_P2_FCz_newnets <- (p.measures(combo_new, FCz_newnets, window=c(180,280), pol="pos"))
Latency_P2_FC4_newnets <- (p.measures(combo_new, FC4_newnets, window=c(180,280), pol="pos"))
Latency_P2_FC3_newnets <- (p.measures(combo_new, FC3_newnets, window=c(180,280), pol="pos"))
Latency_N2_FCz_newnets <- (p.measures(combo_new, FCz_newnets, window=c(320,520), pol="neg")) 
Latency_N2_FC4_newnets <- (p.measures(combo_new, FC4_newnets, window=c(320,520), pol="neg")) 
Latency_N2_FC3_newnets <- (p.measures(combo_new, FC3_newnets, window=c(320,520), pol="neg")) 
Latency_P3_Pz_newnets <- (p.measures(combo_new, Pz_newnets, window=c(450,750), pol="pos"))
Latency_P3_P4_newnets <- (p.measures(combo_new, P4_newnets, window=c(450,750), pol="pos"))
Latency_P3_P3_newnets <- (p.measures(combo_new, P3_newnets, window=c(450,750), pol="pos"))
# Function `rename_datasets()` that renames the columns "Peak Latency" and "Peak Amplitude" in each data frame from a list of data frames (`dataset_list`)
rename_datasets_latency <- function(dataset_list, new_col_name1, new_col_name2){
for (i in 1:length(dataset_list)){
temp_data <- get(dataset_list[i])
names(temp_data)[names(temp_data) == "Peak Latency"] <- new_col_name1[i]
names(temp_data)[names(temp_data) == "Peak Amplitude"] <- new_col_name2[i]
assign(dataset_list[i], temp_data, envir = .GlobalEnv)
}
}
datasets <- c("Latency_P2_FCz_newnets", "Latency_P2_FC4_newnets", "Latency_P2_FC3_newnets",
"Latency_N2_FCz_newnets", "Latency_N2_FC4_newnets", "Latency_N2_FC3_newnets",
"Latency_P3_Pz_newnets", "Latency_P3_P4_newnets", "Latency_P3_P3_newnets")
new_column_names1 <- c("Latency_P2_FCz", "Latency_P2_FC4", "Latency_P2_FC3",
"Latency_N2_FCz", "Latency_N2_FC4", "Latency_N2_FC3",
"Latency_P3_Pz", "Latency_P3_P4", "Latency_P3_P3")
new_column_names2 <- c("PeakAmp_P2_FCz", "PeakAmp_P2_FC4", "PeakAmp_P2_FC3",
"PeakAmp_N2_FCz", "PeakAmp_N2_FC4", "PeakAmp_N2_FC3",
"PeakAmp_P3_Pz", "PeakAmp_P3_P4", "PeakAmp_P3_P3")
rename_datasets_latency(datasets, new_column_names1, new_column_names2)
datasets_list <- mget(datasets)
merge_datasets_latency <- function(datasets_list) {
Reduce(function(x, y) {
merge(x, y, by=c("Subject", "Trial Type"))
}, datasets_list)
}
# Run the function
df_merge2 <- merge_datasets_latency(datasets_list) # use the function to merge the new data frames
# Combine the 2 dataframes
ERP_newnets <- full_join(df_merge1, df_merge2)## Joining with `by = join_by(Subject, `Trial Type`)`head(ERP_newnets)## Subject Trial Type MeanAmp_P2_FCz MeanAmp_P2_FC4 MeanAmp_P2_FC3
## 1 AE050318 NegGo -8.6567894 -10.997870 -8.274136
## 2 AE050318 NegNoGo 0.1368089 3.073859 -10.233068
## 3 AE050318 NeutGo -11.0096655 -10.983267 -8.061495
## 4 AE050318 NeutNoGo -8.0131387 -7.947222 -8.404834
## 5 AH101121 NegGo -6.6852204 -9.178219 -7.760717
## 6 AH101121 NegNoGo -2.2374154 -4.678083 -4.003878
## MeanAmp_N2_FCz MeanAmp_N2_FC4 MeanAmp_N2_FC3 MeanAmp_P3_Pz MeanAmp_P3_P4
## 1 -17.640501 -17.0612959 -12.45291 4.369997 -1.0128204
## 2 -2.763603 -0.6563204 -11.78551 3.345500 0.4492580
## 3 -18.544452 -17.9227477 -12.32419 4.253425 -0.6663896
## 4 -14.488076 -16.2244929 -12.51434 6.818025 -1.6119402
## 5 -22.625932 -24.2391735 -23.10149 18.547604 11.2572051
## 6 -22.243306 -20.8193210 -24.12986 14.173249 11.9982319
## MeanAmp_P3_P3 Latency_P2_FCz PeakAmp_P2_FCz Latency_P2_FC4 PeakAmp_P2_FC4
## 1 7.006061 212 -6.2425933 208 -8.224361
## 2 2.221935 252 3.3000709 232 5.512485
## 3 6.126728 220 -8.4652572 220 -9.281374
## 4 12.549254 216 -4.2941628 216 -3.940464
## 5 16.239133 228 -5.6526816 188 -7.083663
## 6 5.765768 240 -0.5854749 204 -1.564159
## Latency_P2_FC3 PeakAmp_P2_FC3 Latency_N2_FCz PeakAmp_N2_FCz Latency_N2_FC4
## 1 200 -6.982536 352 -23.058663 352
## 2 188 -8.637317 452 -5.257907 400
## 3 212 -6.498847 384 -22.615500 384
## 4 180 -6.557909 320 -20.985998 320
## 5 236 -6.305076 492 -25.080314 484
## 6 240 -1.848016 396 -24.552156 384
## PeakAmp_N2_FC4 Latency_N2_FC3 PeakAmp_N2_FC3 Latency_P3_Pz PeakAmp_P3_Pz
## 1 -20.626624 368 -17.04337 612 6.037603
## 2 -3.018455 408 -12.41742 600 6.301873
## 3 -21.637845 388 -14.78690 592 6.610696
## 4 -21.846657 324 -16.75670 640 10.395567
## 5 -27.616219 520 -26.08764 528 23.899982
## 6 -27.499941 472 -26.48135 508 18.808306
## Latency_P3_P4 PeakAmp_P3_P4 Latency_P3_P3 PeakAmp_P3_P3
## 1 576 0.8441270 512 9.862223
## 2 508 3.1160960 648 5.847591
## 3 488 0.3001207 516 8.529244
## 4 508 0.4807528 524 14.750284
## 5 536 15.0651712 512 20.561269
## 6 584 16.8901785 652 4.791160# remove standard dev column:
# ERP_newnets <- ERP_newnets %>% select(-`Standard Dev`)REPEAT FOR OLD NETS!
MeanAmp_P2_FCz_oldnets <- (m.measures(combo_old, FCz_oldnets, window=c(180,280)))
MeanAmp_P2_FC4_oldnets <- (m.measures(combo_old, FC4_oldnets, window=c(180,280)))
MeanAmp_P2_FC3_oldnets <- (m.measures(combo_old, FC3_oldnets, window=c(180,280)))
MeanAmp_N2_FCz_oldnets <- (m.measures(combo_old, FCz_oldnets, window=c(320,520))) 
MeanAmp_N2_FC4_oldnets <- (m.measures(combo_old, FC4_oldnets, window=c(320,520))) 
MeanAmp_N2_FC3_oldnets <- (m.measures(combo_old, FC3_oldnets, window=c(320,520))) 
MeanAmp_P3_Pz_oldnets <- (m.measures(combo_old, Pz_oldnets, window=c(450,750))) 
MeanAmp_P3_P4_oldnets <- (m.measures(combo_old, P4_oldnets, window=c(450,750))) 
MeanAmp_P3_P3_oldnets <- (m.measures(combo_old, P3_oldnets, window=c(450,750))) 
datasets <- c("MeanAmp_P2_FCz_oldnets", "MeanAmp_P2_FC4_oldnets", "MeanAmp_P2_FC3_oldnets",
"MeanAmp_N2_FCz_oldnets", "MeanAmp_N2_FC4_oldnets", "MeanAmp_N2_FC3_oldnets",
"MeanAmp_P3_Pz_oldnets", "MeanAmp_P3_P4_oldnets", "MeanAmp_P3_P3_oldnets")
new_column_names <- c("MeanAmp_P2_FCz", "MeanAmp_P2_FC4", "MeanAmp_P2_FC3",
"MeanAmp_N2_FCz", "MeanAmp_N2_FC4", "MeanAmp_N2_FC3",
"MeanAmp_P3_Pz", "MeanAmp_P3_P4", "MeanAmp_P3_P3")
rename_datasets_amplitude(datasets, new_column_names)
datasets_list <- mget(datasets)
df_merge1_old <- merge_datasets_amplitude(datasets_list) # use the function to merge the new data frames
Latency_P2_FCz_oldnets <- (p.measures(combo_old, FCz_oldnets, window=c(180,280), pol="pos"))
Latency_P2_FC4_oldnets <- (p.measures(combo_old, FC4_oldnets, window=c(180,280), pol="pos"))
Latency_P2_FC3_oldnets <- (p.measures(combo_old, FC3_oldnets, window=c(180,280), pol="pos"))
Latency_N2_FCz_oldnets <- (p.measures(combo_old, FCz_oldnets, window=c(320,520), pol="neg")) 
Latency_N2_FC4_oldnets <- (p.measures(combo_old, FC4_oldnets, window=c(320,520), pol="neg")) 
Latency_N2_FC3_oldnets <- (p.measures(combo_old, FC3_oldnets, window=c(320,520), pol="neg")) 
Latency_P3_Pz_oldnets <- (p.measures(combo_old, Pz_oldnets, window=c(450,750), pol="pos"))
Latency_P3_P4_oldnets <- (p.measures(combo_old, P4_oldnets, window=c(450,750), pol="pos"))
Latency_P3_P3_oldnets <- (p.measures(combo_old, P3_oldnets, window=c(450,750), pol="pos"))
datasets <- c("Latency_P2_FCz_oldnets", "Latency_P2_FC4_oldnets", "Latency_P2_FC3_oldnets",
"Latency_N2_FCz_oldnets", "Latency_N2_FC4_oldnets", "Latency_N2_FC3_oldnets",
"Latency_P3_Pz_oldnets", "Latency_P3_P4_oldnets", "Latency_P3_P3_oldnets")
new_column_names1 <- c("Latency_P2_FCz", "Latency_P2_FC4", "Latency_P2_FC3",
"Latency_N2_FCz", "Latency_N2_FC4", "Latency_N2_FC3",
"Latency_P3_Pz", "Latency_P3_P4", "Latency_P3_P3")
new_column_names2 <- c("PeakAmp_P2_FCz", "PeakAmp_P2_FC4", "PeakAmp_P2_FC3",
"PeakAmp_N2_FCz", "PeakAmp_N2_FC4", "PeakAmp_N2_FC3",
"PeakAmp_P3_Pz", "PeakAmp_P3_P4", "PeakAmp_P3_P3")
rename_datasets_latency(datasets, new_column_names1, new_column_names2)
datasets_list <- mget(datasets)
df_merge2_old <- merge_datasets_latency(datasets_list) # use the function to merge the new data frames
# Combine the 2 dataframes
ERP_oldnets <- full_join(df_merge1_old, df_merge2_old)## Joining with `by = join_by(Subject, `Trial Type`)`# Combine old +new
ERP <- full_join(ERP_oldnets, ERP_newnets)## Joining with `by = join_by(Subject, `Trial Type`, MeanAmp_P2_FCz,
## MeanAmp_P2_FC4, MeanAmp_P2_FC3, MeanAmp_N2_FCz, MeanAmp_N2_FC4, MeanAmp_N2_FC3,
## MeanAmp_P3_Pz, MeanAmp_P3_P4, MeanAmp_P3_P3, Latency_P2_FCz, PeakAmp_P2_FCz,
## Latency_P2_FC4, PeakAmp_P2_FC4, Latency_P2_FC3, PeakAmp_P2_FC3, Latency_N2_FCz,
## PeakAmp_N2_FCz, Latency_N2_FC4, PeakAmp_N2_FC4, Latency_N2_FC3, PeakAmp_N2_FC3,
## Latency_P3_Pz, PeakAmp_P3_Pz, Latency_P3_P4, PeakAmp_P3_P4, Latency_P3_P3,
## PeakAmp_P3_P3)`# Remove Grand Ave from data, order by subject name and reset the index:
ERP <- ERP[!(ERP$Subject=="Grand Avg"),]
ERP <- with(ERP, ERP[order(Subject) , ])
rownames(ERP) <- NULL # Reset indexMORE RE-FORMATTING:
# CREATE A NEW COLUMN by taking the difference between N2-P2
ERP$MeanAmp_N2P2_FCz <- ERP$MeanAmp_N2_FCz - ERP$MeanAmp_P2_FCz
ERP$PeakAmp_N2P2_FCz <- ERP$PeakAmp_N2_FCz - ERP$PeakAmp_P2_FCz
ERP$MeanAmp_N2P2_FC4 <- ERP$MeanAmp_N2_FC4 - ERP$MeanAmp_P2_FC4
ERP$PeakAmp_N2P2_FC4 <- ERP$PeakAmp_N2_FC4 - ERP$PeakAmp_P2_FC4
ERP$MeanAmp_N2P2_FC3 <- ERP$MeanAmp_N2_FC3 - ERP$MeanAmp_P2_FC3
ERP$PeakAmp_N2P2_FC3 <- ERP$PeakAmp_N2_FC3 - ERP$PeakAmp_P2_FC3
# REORGANIZE STIMTAG VARIABLE AS TWO SEPERATE VARIABLES: EMOTION AND CONDITION - EACH WITH TWO LEVELS
ERP <- ERP %>%
mutate(Emotion = str_extract(`Trial Type`, "Neut|Neg"),
Condition = str_extract(`Trial Type`, "Go|NoGo"))
# RESHAPE TO LONG FORMAT
ERP_long <- pivot_longer(ERP,
cols = -c(Subject, `Trial Type`, `Condition`, `Emotion`),
names_to = c(".value", "Electrode"),
names_pattern = "^(MeanAmp_P2|MeanAmp_N2|MeanAmp_N2P2|MeanAmp_P3|PeakAmp_P2|PeakAmp_N2|PeakAmp_N2P2|PeakAmp_P3|Latency_P2|Latency_N2|Latency_P3)_(.+)$",
values_to = "Value"
) %>%
mutate(
Region = ifelse(grepl("^FC", Electrode), "Frontocentral", "Parietal"),
Laterality = ifelse(grepl("4", Electrode), "Right",
ifelse(grepl("3", Electrode), "Left", "Midline"))
) %>%
select(-Electrode)
ERP_long <- pivot_longer(ERP,
cols = -c(Subject, `Trial Type`, `Condition`, `Emotion`),
names_to = c(".value", "Electrode"),
names_pattern = "^(MeanAmp_P2|MeanAmp_N2|MeanAmp_N2P2|MeanAmp_P3|PeakAmp_P2|PeakAmp_N2|PeakAmp_N2P2|PeakAmp_P3|Latency_P2|Latency_N2|Latency_P3)_(.+)$",
values_to = "Value"
) %>%
mutate(
Laterality = ifelse(grepl("4", Electrode), "Right",
ifelse(grepl("3", Electrode), "Left", "Midline"))
) %>%
select(-Electrode)
ERP_long <- ERP_long %>%
group_by(Subject, `Trial Type`, Emotion, Condition, Laterality) %>%
summarise(across(everything(), ~mean(., na.rm = TRUE)), .groups = 'drop')
# Write to a csv file:
write.csv(ERP_long, "/Users/aysuerdemir/Desktop/R workspace/ERP_Zoo/CrossSectional/Mix/ERP_long")
head(ERP_long)## # A tibble: 6 × 16
## Subject `Trial Type` Emotion Condition Laterality MeanAmp_P2 MeanAmp_N2
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 AE050318 NegGo Neg Go Left -8.27 -12.5
## 2 AE050318 NegGo Neg Go Midline -8.66 -17.6
## 3 AE050318 NegGo Neg Go Right -11.0 -17.1
## 4 AE050318 NegNoGo Neg NoGo Left -10.2 -11.8
## 5 AE050318 NegNoGo Neg NoGo Midline 0.137 -2.76
## 6 AE050318 NegNoGo Neg NoGo Right 3.07 -0.656
## # ℹ 9 more variables: MeanAmp_P3 <dbl>, Latency_P2 <dbl>, PeakAmp_P2 <dbl>,
## # Latency_N2 <dbl>, PeakAmp_N2 <dbl>, Latency_P3 <dbl>, PeakAmp_P3 <dbl>,
## # MeanAmp_N2P2 <dbl>, PeakAmp_N2P2 <dbl>LOAD AND MERGE WITH INTAKE INFO:
- Load data exported from RedCap, named: Intake_Stuttering_Language_Varbls_for_Zoo_and_Reactivity
- Feature engineer necessary variables and merge the above ERP dataset with TalkerGroup, Gender, Age info along with Stuttering and Language Scores
# Load DataSet:
intake <- read.csv(file = '/Users/aysuerdemir/Desktop/R workspace/ERP_Zoo/CognitiveEmotionalLi-IntakeDiagnosticData_DATA_2023-10-25_0818.csv')
# Subject IDs include the visit number in the combo dataset if it is NOT the first time point.
# Do the same here: Combine visit number with subject and create a new Subject variable so that it matches the combo:
intake <- intake %>%
mutate(Subject = ifelse(redcap_event_name !="t1_arm_1", paste0(part_id_status, "T", visitnumber), part_id_status))
# Create a new variable representing final sldper100words ("disfluency_sldper100words_final) by taking disfluency_sldper100words from CVD as primary,
# but in the case that this data is missing, take the disfluency scores from CVE:
intake <- intake %>%
mutate(disfluency_sldper100words_final = ifelse(!is.na(disfluency_sldper100words), disfluency_sldper100words, disfluency_sldper100words_cve))
# Create a final talker group variable ("talkergroup_final) using disfluency_sldper100words_final and talker group based on parent report:
# 1: CWS, 0:CWNS, 9:unidentified
intake <- intake %>%
mutate(talkergroup_final = ifelse((disfluency_sldper100words_final >= 3 | calculator_talkergroup_parent == 1), 1,
ifelse((disfluency_sldper100words_final < 3 & calculator_talkergroup_parent == 0), 0, 9)))
# Take the relevant columns from intake dataset
# You may update this to take more columns into the dataset:
intake <- subset(intake, select=c('Subject','calculator_age_cve','calculator_gender_cve','race', 'ethnicity',
'calculator_talkergroup_parent','tso_calculated',
'disfluency_sldper100words','ssi_total',
'disfluency_sldper100words_final', 'talkergroup_final',
"gfta_standard", "ppvt_standard", "evt_standard",
'teld_rec_standard','teld_exp_standard', "teld_spokenlang_standard",
'tocs_1_total', 'tocs_2_total', 'tcs_total',
'eprime_condorder_zootask','cve_comments','comments_tasks','handedness_zoo'))
# Merge with the main dataset using SUBJECT
FULL <- merge(ERP_long, intake, by=c("Subject"), all.x = TRUE)
head(FULL) ## Subject Trial Type Emotion Condition Laterality MeanAmp_P2 MeanAmp_N2
## 1 AE050318 NegGo Neg Go Left -8.2741357 -12.4529149
## 2 AE050318 NegGo Neg Go Midline -8.6567894 -17.6405009
## 3 AE050318 NegGo Neg Go Right -10.9978696 -17.0612959
## 4 AE050318 NegNoGo Neg NoGo Left -10.2330683 -11.7855128
## 5 AE050318 NegNoGo Neg NoGo Midline 0.1368089 -2.7636031
## 6 AE050318 NegNoGo Neg NoGo Right 3.0738593 -0.6563204
## MeanAmp_P3 Latency_P2 PeakAmp_P2 Latency_N2 PeakAmp_N2 Latency_P3 PeakAmp_P3
## 1 7.006061 200 -6.982536 368 -17.043367 512 9.862223
## 2 4.369997 212 -6.242593 352 -23.058663 612 6.037603
## 3 -1.012820 208 -8.224361 352 -20.626624 576 0.844127
## 4 2.221935 188 -8.637317 408 -12.417423 648 5.847591
## 5 3.345500 252 3.300071 452 -5.257907 600 6.301873
## 6 0.449258 232 5.512485 400 -3.018455 508 3.116096
## MeanAmp_N2P2 PeakAmp_N2P2 calculator_age_cve calculator_gender_cve race
## 1 -4.178779 -10.060831 38.1 0 2
## 2 -8.983711 -16.816070 38.1 0 2
## 3 -6.063426 -12.402263 38.1 0 2
## 4 -1.552445 -3.780106 38.1 0 2
## 5 -2.900412 -8.557978 38.1 0 2
## 6 -3.730180 -8.530940 38.1 0 2
## ethnicity calculator_talkergroup_parent tso_calculated
## 1 0 1 1.9
## 2 0 1 1.9
## 3 0 1 1.9
## 4 0 1 1.9
## 5 0 1 1.9
## 6 0 1 1.9
## disfluency_sldper100words ssi_total disfluency_sldper100words_final
## 1 12 23 12
## 2 12 23 12
## 3 12 23 12
## 4 12 23 12
## 5 12 23 12
## 6 12 23 12
## talkergroup_final gfta_standard ppvt_standard evt_standard teld_rec_standard
## 1 1 121 126 123 146
## 2 1 121 126 123 146
## 3 1 121 126 123 146
## 4 1 121 126 123 146
## 5 1 121 126 123 146
## 6 1 121 126 123 146
## teld_exp_standard teld_spokenlang_standard tocs_1_total tocs_2_total
## 1 135 149 22 6
## 2 135 149 22 6
## 3 135 149 22 6
## 4 135 149 22 6
## 5 135 149 22 6
## 6 135 149 22 6
## tcs_total eprime_condorder_zootask cve_comments comments_tasks handedness_zoo
## 1 25 1 NA
## 2 25 1 NA
## 3 25 1 NA
## 4 25 1 NA
## 5 25 1 NA
## 6 25 1 NA# Print the unique subject codes
unique_codes<- unique(FULL$Subject)
unique_codes <- as.data.frame(unique_codes)
unique_codes## unique_codes
## 1 AE050318
## 2 AH101121
## 3 AK022218
## 4 AK102221
## 5 AL041819
## 6 AN122116
## 7 AS110816
## 8 AT051818
## 9 AW040217
## 10 AW110418
## 11 BW071018T2
## 12 CC031323
## 13 CC102318T2
## 14 CF101019
## 15 CL040218
## 16 CM101322
## 17 CM120919
## 18 DJ052417
## 19 EC041817
## 20 EG030618
## 21 EM100417
## 22 ES031519
## 23 ES032018
## 24 FM032823
## 25 FW121816
## 26 GB012717
## 27 GR091721T2
## 28 HC102117
## 29 HC111621
## 30 HH061919
## 31 HW110822
## 32 JA092118
## 33 JG091119T3
## 34 JJ011018
## 35 JK032119T3
## 36 JM041823
## 37 JS121321
## 38 JT051618
## 39 KA022017
## 40 KE050718
## 41 KT072319
## 42 LB012619
## 43 LB111121
## 44 LG100721
## 45 LO042723
## 46 LT112916
## 47 LW102219T3
## 48 MF101019
## 49 MM040119
## 50 MR091118T2
## 51 MS102319
## 52 NL041119
## 53 OB032423
## 54 OG013016
## 55 OJ032223
## 56 PB021519
## 57 PB031723
## 58 PW030417
## 59 PW041023
## 60 RB041423
## 61 RB101619
## 62 RC102022
## 63 RH100218
## 64 RK040219T3
## 65 RO042723
## 66 RS030518
## 67 RT032219
## 68 RT042523
## 69 SB111121
## 70 SC051023
## 71 SK041519
## 72 SL090418
## 73 SP010219
## 74 ST100121T2
## 75 TA051917
## 76 TE062818
## 77 TS011518
## 78 WB110221
## 79 WF080417
## 80 WH022219
## 81 WK011122
## 82 WS051018# Making sure the Final FULL dataset has the all subjects coded (initially specified by SUBS - subject number):
subs = 82
(nrow(FULL)/12) == subs # This should give TRUE! 12 rows per subject## [1] TRUEShow the rows where talkergroup_final = 9 or NA :
undefined_talkergroup <- subset(FULL, talkergroup_final == 9 | is.na(talkergroup_final)) print(unique(undefined_talkergroup$Subject))
FIND THE UNDEFINED (9) TALKER GROUPS AND MANUALLY MARK THEM AS EITHER 1 or 0 if needed:
Replace NA values in a specific column based on a condition:
FULL\(talkergroup_final <- ifelse(FULL\)Subject == “JA092118”, 0, FULL\(talkergroup_final) FULL\)talkergroup_final <- ifelse(FULL\(Subject == "LG100721T2", 1, FULL\)talkergroup_final) FULL\(talkergroup_final <- ifelse(FULL\)Subject == “LW102219T3”, 1, FULL$talkergroup_final)
Making sure no 9 or NA remained:
any(FULL\(talkergroup_final == 9 | is.na(FULL\)talkergroup_final))
# How many kids in each group?
talkergroup_counts <- table(FULL$talkergroup_final)
print(talkergroup_counts)/12##
## 0 1
## 504 480##
## 0 1
## 42 40ALSO ADD THE AVAILABLE LONGITUDINAL CATEGORIES:
Load DataSet:
longitudinal_groups <- read.csv(file = ‘/Users/aysuerdemir/Desktop/R workspace/ERP_Zoo/LongitudinalClassifications_9.10.23.csv’)
Merge with the main dataset using SUBJECT
FULL <- merge(FULL, longitudinal_groups[, c(“Subject”, “Longitudinal_Group_0Rec_1Per_2Und”)], by=c(“Subject”), all.x = TRUE)
Making sure the Final FULL dataset has the all subjects coded (initially specified by SUBS - subject number):
(nrow(FULL)/4) == subs # This should give TRUE!
How many kids in each group?
longtgroup_counts <- table(FULL$Longitudinal_Group_0Rec_1Per_2Und) print(longtgroup_counts)/4
LOAD AND MERGE WITH BEHAVIORAL DATA:
- for GO and NOGO conditions use ShowStim.RESP to compute the accuracy.
- ShowStim.RESP: 4 it means the child pushed the button (accurate for Go), NA means no response (accurate for NoGo).
Trials with responses faster than 200 ms were eliminated from the analysis, as they were too quick to reflect responding to the current stimulus.
Go proportion correct 0.75 0.185 0.07–1.0 )1.03 0.76 Go RT (ms) 934 155.5 378–1,300 0.03 )0.29 No-go proportion correct 0.74 0.312 0–1 )1.16 0.02 Sensitivity (d¢) 1.54 1.114 )1.27–3.77 )0.44 )0.45
Mean reaction times in ms for CWS and CWNS. Group Hits False alarms M SD M SD CWS 509 132 382* 111 CWNS 534 104 457* 145
For each participant, the frequency of the following variables was automatically recorded and stored: (a) ‘hits’ (when a Go-stimulus was followed by a response falling between 200 and 2300 ms after stimulus onset), (b) ‘misses’ (when a Gostimulus was not followed by a response), (c) ‘false alarms’ (when a NoGo-stimulus was followed by pressing the response button between 200 and 2300 ms after stimulus onset), and (d) ‘premature responses’ (when the response button was pressed between 0 and 200 ms after stimulus onset). In addition, for the variables ‘hits’ and ‘false alarms’ mean RTs were also recorded.
In case a child exhibited a false alarm or premature response on two or more trials out of the 48 trials, this was defined as exhibiting ‘multiple false alarms’ or ‘multiple premature responses’. In addition, for the variables ‘hits’ and ‘false alarms’ mean RTs were also recorded.
# Load the file:
accuracy <- read.csv(file = '/Users/aysuerdemir/Desktop/R workspace/ERP_Zoo/Merged_Zoo_10.03.23.csv')
# Take only the relevant variables:
accuracy <- subset(accuracy, select=c('Name','VisitNumber','ShowStim.ACC','ShowStim.RESP','ShowStim.RT','StimTag'))
# Convert all empty strings to NA
accuracy <- replace(accuracy, accuracy == "", NA)
# accuracy$ShowStim.RESP <- na_if(accuracy$ShowStim.RESP, "")
# Combine visit number with subject and create a new Subject variable for eprime so that it matches the FULL
accuracy <- accuracy %>%
mutate(Subject = ifelse(VisitNumber != 1, paste0(Name, "T", VisitNumber), Name))
# Print unique subjects in FULL
unique_subjects <- unique(FULL$Subject)
# Filter 'accuracy' dataframe
accuracy <- accuracy[accuracy$`Subject` %in% unique_subjects, ]
# Unique counts for ShowStim.RESP
unique_codes <- unique(accuracy$ShowStim.RESP)
counts <- as.data.frame(table(accuracy$ShowStim.RESP))
counts## Var1 Freq
## 1 {ALT} 1
## 2 {CONTROL} 8
## 3 {SHIFT} 1
## 4 {SPACE} 3
## 5 1 5
## 6 2 2
## 7 3 53
## 8 4 20225
## 9 6 11# Check out the class types for each variable.
sapply(accuracy, class)## Name VisitNumber ShowStim.ACC ShowStim.RESP ShowStim.RT
## "character" "integer" "integer" "character" "integer"
## StimTag Subject
## "character" "character"# For ShowStim.RESP response 4 is a "character", not integer.
print(class(accuracy$ShowStim.RESP))## [1] "character"# Convert character 4 for ShowStim.RESP to integer
# accuracy$ShowStim.RESP <- as.integer(accuracy$ShowStim.RESP)
# For each participant, the frequency of the following variables was automatically recorded and stored:
# (a) ‘hits’ (when a Go-stimulus was followed by a response falling between 200 and 2300 ms after stimulus onset),
# (b) ‘misses’ (when a Go stimulus was not followed by a response),
# (c) ‘false alarms’ (when a NoGo-stimulus was followed by pressing the response button between 200 and 2300 ms after stimulus onset), and
# (d) ‘premature responses’ (when the response button was pressed between 0 and 200 ms after stimulus onset).
# Remove the special character trials, and only keep those trials with proper number resonses.
unique(accuracy$ShowStim.RESP)## [1] "4" NA "1" "{SPACE}" "3" "{CONTROL}"
## [7] "{SHIFT}" "2" "{ALT}" "6"accuracy <- accuracy[is.na(accuracy$ShowStim.RESP) | accuracy$ShowStim.RESP %in% c(1,2,3,4,5,6) , ]
# Drop the 2 rows with NA:
accuracy <- accuracy[!is.na(accuracy$StimTag), ]
# CREATE THE NEW VARIABLES:
# Our max RT is 2023
accuracy <- accuracy %>%
mutate(behavior = case_when(
(ShowStim.RT >= 200 & ShowStim.RESP == 4) & (StimTag %in% c("negG", "neuG")) ~ "correct_go",
(ShowStim.RT == 0) & (StimTag %in% c("negG", "neuG")) ~ "wrong_go",
(ShowStim.RT < 200 & ShowStim.RT > 0 & ShowStim.RESP %in% 1:6) & (StimTag %in% c("negG", "neuG")) ~ "premature_go",
(ShowStim.RT == 0 & (StimTag %in% c("negN", "neuN"))) ~ "correct_nogo",
(ShowStim.RT > 200 & ShowStim.RESP %in% 1:6) & (StimTag %in% c("negN", "neuN")) ~ "wrong_nogo_falsealarm",
(ShowStim.RT < 200 & ShowStim.RT > 0 & ShowStim.RESP %in% 1:6) & (StimTag %in% c("negN", "neuN")) ~ "premature_nogo",
TRUE ~ NA_character_ # is a catch-all condition that assigns `NA` to any rows that don't meet any of the previous conditions
))
# Calculate the proportions
accuracy_proportions <- accuracy %>%
group_by(Subject, StimTag) %>%
summarise(
correct_go = sum(behavior == "correct_go", na.rm = TRUE),
wrong_go = sum(behavior == "wrong_go", na.rm = TRUE),
premature_go = sum(behavior == "premature_go", na.rm = TRUE),
accuracy_go_proportion = (correct_go / (correct_go + wrong_go +premature_go)) *100 ,
correct_nogo = sum(behavior == "correct_nogo", na.rm = TRUE),
wrong_nogo_falsealarm = sum(behavior == "wrong_nogo_falsealarm", na.rm = TRUE),
premature_nogo = sum(behavior == "premature_nogo", na.rm = TRUE),
accuracy_nogo_proportion = (correct_nogo / (correct_nogo + wrong_nogo_falsealarm +premature_nogo))*100,
premature_go = sum(behavior == "premature_go", na.rm = TRUE),
premature_go_proportion = (premature_go / (correct_go + premature_go))*100,
premature_nogo = sum(behavior == "premature_nogo", na.rm = TRUE),
premature_nogo_proportion = (premature_nogo / (wrong_nogo_falsealarm + premature_nogo))*100,
RT_proper_go = mean(ifelse(ShowStim.RT >= 200 & (StimTag == 'neuG' | StimTag == 'negG'), ShowStim.RT, NA), na.rm = TRUE),
RT_all_go = mean(ifelse((StimTag == 'neuG' | StimTag == 'negG') & ShowStim.RT != 0, ShowStim.RT, NA), na.rm = TRUE),
RT_nogo_falsealarm = mean(ifelse((behavior == 'wrong_nogo_falsealarm'), ShowStim.RT, NA), na.rm = TRUE)
)## `summarise()` has grouped output by 'Subject'. You can override using the
## `.groups` argument.# Combine accuracy go and accuracy nogo together, premature go and nogo together, and RT go and RT nogo together:
# Since 'accuracy_go' and 'accuracy_nogo' do not overlap (one of them is always NA for a given row), you can use the `coalesce` function to pick the non-NA value
accuracy_proportions_final <- accuracy_proportions %>%
mutate(
accuracy = coalesce(accuracy_go_proportion, accuracy_nogo_proportion),
premature_responses = coalesce(premature_go_proportion, premature_nogo_proportion),
RT_proper = coalesce(RT_proper_go, RT_nogo_falsealarm))
# Subset to main variables only
accuracy_proportions_final <- subset(accuracy_proportions_final, select=c('Subject', "StimTag", "accuracy", "premature_responses", "RT_proper", "RT_all_go"))
# Rename the labels for StimTags on eprime data
eprime <- accuracy_proportions_final %>%
mutate(StimTag = recode(StimTag, "negG" = "NegGo", "negN" = "NegNoGo", "neuG" = "NeutGo", "neuN" = "NeutNoGo"))
head(eprime)## # A tibble: 6 × 6
## # Groups: Subject [2]
## Subject StimTag accuracy premature_responses RT_proper RT_all_go
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 AE050318 NegGo 75.8 9.90 774. 706.
## 2 AE050318 NegNoGo 57.5 23.5 566 NaN
## 3 AE050318 NeutGo 90 0.917 802. 796.
## 4 AE050318 NeutNoGo 70 16.7 721 NaN
## 5 AH101121 NegGo 99.2 0 643. 643.
## 6 AH101121 NegNoGo 87.5 0 411. NaNCOMBINE FULL WITH EPRIME
# Replace Trial Type in FULL with "StimTag" to be able to merge with eprime data
FULL <- FULL %>%
dplyr::rename("StimTag" = "Trial Type")
# COMBINE ALL!!
ZOO <- merge(FULL, eprime, by=c("Subject", "StimTag"), all.x = TRUE)
head(ZOO)## Subject StimTag Emotion Condition Laterality MeanAmp_P2 MeanAmp_N2
## 1 AE050318 NegGo Neg Go Left -8.2741357 -12.4529149
## 2 AE050318 NegGo Neg Go Midline -8.6567894 -17.6405009
## 3 AE050318 NegGo Neg Go Right -10.9978696 -17.0612959
## 4 AE050318 NegNoGo Neg NoGo Right 3.0738593 -0.6563204
## 5 AE050318 NegNoGo Neg NoGo Midline 0.1368089 -2.7636031
## 6 AE050318 NegNoGo Neg NoGo Left -10.2330683 -11.7855128
## MeanAmp_P3 Latency_P2 PeakAmp_P2 Latency_N2 PeakAmp_N2 Latency_P3 PeakAmp_P3
## 1 7.006061 200 -6.982536 368 -17.043367 512 9.862223
## 2 4.369997 212 -6.242593 352 -23.058663 612 6.037603
## 3 -1.012820 208 -8.224361 352 -20.626624 576 0.844127
## 4 0.449258 232 5.512485 400 -3.018455 508 3.116096
## 5 3.345500 252 3.300071 452 -5.257907 600 6.301873
## 6 2.221935 188 -8.637317 408 -12.417423 648 5.847591
## MeanAmp_N2P2 PeakAmp_N2P2 calculator_age_cve calculator_gender_cve race
## 1 -4.178779 -10.060831 38.1 0 2
## 2 -8.983711 -16.816070 38.1 0 2
## 3 -6.063426 -12.402263 38.1 0 2
## 4 -3.730180 -8.530940 38.1 0 2
## 5 -2.900412 -8.557978 38.1 0 2
## 6 -1.552445 -3.780106 38.1 0 2
## ethnicity calculator_talkergroup_parent tso_calculated
## 1 0 1 1.9
## 2 0 1 1.9
## 3 0 1 1.9
## 4 0 1 1.9
## 5 0 1 1.9
## 6 0 1 1.9
## disfluency_sldper100words ssi_total disfluency_sldper100words_final
## 1 12 23 12
## 2 12 23 12
## 3 12 23 12
## 4 12 23 12
## 5 12 23 12
## 6 12 23 12
## talkergroup_final gfta_standard ppvt_standard evt_standard teld_rec_standard
## 1 1 121 126 123 146
## 2 1 121 126 123 146
## 3 1 121 126 123 146
## 4 1 121 126 123 146
## 5 1 121 126 123 146
## 6 1 121 126 123 146
## teld_exp_standard teld_spokenlang_standard tocs_1_total tocs_2_total
## 1 135 149 22 6
## 2 135 149 22 6
## 3 135 149 22 6
## 4 135 149 22 6
## 5 135 149 22 6
## 6 135 149 22 6
## tcs_total eprime_condorder_zootask cve_comments comments_tasks handedness_zoo
## 1 25 1 NA
## 2 25 1 NA
## 3 25 1 NA
## 4 25 1 NA
## 5 25 1 NA
## 6 25 1 NA
## accuracy premature_responses RT_proper RT_all_go
## 1 75.83333 9.90099 774.044 706.0297
## 2 75.83333 9.90099 774.044 706.0297
## 3 75.83333 9.90099 774.044 706.0297
## 4 57.50000 23.52941 566.000 NaN
## 5 57.50000 23.52941 566.000 NaN
## 6 57.50000 23.52941 566.000 NaNSee how many kids have missing accuracy data and manually add them:
ADD THIS KID MANUALLY _ ACCURACY TAKEN FROM NETSTATION:
# See how many kids have missing accuracy data and manually add them:
subset_NA_eprime_data <- subset(ZOO, is.na(ZOO$accuracy))
unique(subset_NA_eprime_data$Subject)## [1] "RB101619"# Identify the specific row
rows_to_update_NeutNoGo <- which(ZOO$Subject == "RB101619" & ZOO$StimTag == "NeutNoGo")
rows_to_update_NeutGo <- which(ZOO$Subject == "RB101619" & ZOO$StimTag == "NeutGo")
rows_to_update_NegNoGo <- which(ZOO$Subject == "RB101619" & ZOO$StimTag == "NegNoGo")
rows_to_update_NegGo <- which(ZOO$Subject == "RB101619" & ZOO$StimTag == "NegGo")
# Replace the accuracy value
ZOO$accuracy[rows_to_update_NeutNoGo] <- 50
ZOO$accuracy[rows_to_update_NeutGo] <- 99
ZOO$accuracy[rows_to_update_NegNoGo] <- 42.5
ZOO$accuracy[rows_to_update_NegGo] <- 99
ZOO[ZOO$Subject == "RB101619",]## Subject StimTag Emotion Condition Laterality MeanAmp_P2 MeanAmp_N2
## 721 RB101619 NegGo Neg Go Midline -5.390026 -17.650182
## 722 RB101619 NegGo Neg Go Right -10.797848 -21.432989
## 723 RB101619 NegGo Neg Go Left -3.950041 -14.275685
## 724 RB101619 NegNoGo Neg NoGo Left -6.000802 -14.086998
## 725 RB101619 NegNoGo Neg NoGo Right -5.192217 -13.254804
## 726 RB101619 NegNoGo Neg NoGo Midline -4.264043 -14.067967
## 727 RB101619 NeutGo Neut Go Left -2.396887 -9.280133
## 728 RB101619 NeutGo Neut Go Midline -1.797148 -10.750121
## 729 RB101619 NeutGo Neut Go Right -6.212510 -14.489684
## 730 RB101619 NeutNoGo Neut NoGo Left -2.975609 -8.827129
## 731 RB101619 NeutNoGo Neut NoGo Midline -5.478988 -12.531785
## 732 RB101619 NeutNoGo Neut NoGo Right -9.838859 -21.048845
## MeanAmp_P3 Latency_P2 PeakAmp_P2 Latency_N2 PeakAmp_N2 Latency_P3
## 721 4.185884 216 -4.1729526 404 -20.73456 540
## 722 4.828953 224 -9.1936062 388 -24.14483 516
## 723 9.714811 216 -2.9772724 404 -16.83459 564
## 724 8.974658 224 -1.9849400 340 -19.90115 592
## 725 21.664147 236 -2.8018056 332 -16.99843 728
## 726 5.752708 216 0.8061220 340 -20.08442 588
## 727 5.265058 204 -0.0317794 496 -11.15279 456
## 728 4.319342 204 1.3632187 388 -12.88830 456
## 729 4.932128 204 -3.5492104 384 -16.30434 508
## 730 7.797420 216 0.8018334 368 -12.21802 488
## 731 8.611435 208 -1.0327817 416 -14.68038 452
## 732 5.549722 200 -4.5049230 400 -23.30060 700
## PeakAmp_P3 MeanAmp_N2P2 PeakAmp_N2P2 calculator_age_cve
## 721 6.372755 -12.260156 -16.56161 53.5
## 722 9.144339 -10.635141 -14.95122 53.5
## 723 11.680410 -10.325644 -13.85732 53.5
## 724 14.403730 -8.086196 -17.91621 53.5
## 725 29.863190 -8.062587 -14.19663 53.5
## 726 10.463715 -9.803924 -20.89054 53.5
## 727 8.270745 -6.883246 -11.12101 53.5
## 728 6.482269 -8.952973 -14.25151 53.5
## 729 9.959636 -8.277174 -12.75513 53.5
## 730 12.555105 -5.851520 -13.01985 53.5
## 731 14.208231 -7.052797 -13.64760 53.5
## 732 11.736709 -11.209987 -18.79568 53.5
## calculator_gender_cve race ethnicity calculator_talkergroup_parent
## 721 1 5 0 1
## 722 1 5 0 1
## 723 1 5 0 1
## 724 1 5 0 1
## 725 1 5 0 1
## 726 1 5 0 1
## 727 1 5 0 1
## 728 1 5 0 1
## 729 1 5 0 1
## 730 1 5 0 1
## 731 1 5 0 1
## 732 1 5 0 1
## tso_calculated disfluency_sldper100words ssi_total
## 721 27.5 9.33 28
## 722 27.5 9.33 28
## 723 27.5 9.33 28
## 724 27.5 9.33 28
## 725 27.5 9.33 28
## 726 27.5 9.33 28
## 727 27.5 9.33 28
## 728 27.5 9.33 28
## 729 27.5 9.33 28
## 730 27.5 9.33 28
## 731 27.5 9.33 28
## 732 27.5 9.33 28
## disfluency_sldper100words_final talkergroup_final gfta_standard
## 721 9.33 1 119
## 722 9.33 1 119
## 723 9.33 1 119
## 724 9.33 1 119
## 725 9.33 1 119
## 726 9.33 1 119
## 727 9.33 1 119
## 728 9.33 1 119
## 729 9.33 1 119
## 730 9.33 1 119
## 731 9.33 1 119
## 732 9.33 1 119
## ppvt_standard evt_standard teld_rec_standard teld_exp_standard
## 721 120 117 125 107
## 722 120 117 125 107
## 723 120 117 125 107
## 724 120 117 125 107
## 725 120 117 125 107
## 726 120 117 125 107
## 727 120 117 125 107
## 728 120 117 125 107
## 729 120 117 125 107
## 730 120 117 125 107
## 731 120 117 125 107
## 732 120 117 125 107
## teld_spokenlang_standard tocs_1_total tocs_2_total tcs_total
## 721 119 17 14 24
## 722 119 17 14 24
## 723 119 17 14 24
## 724 119 17 14 24
## 725 119 17 14 24
## 726 119 17 14 24
## 727 119 17 14 24
## 728 119 17 14 24
## 729 119 17 14 24
## 730 119 17 14 24
## 731 119 17 14 24
## 732 119 17 14 24
## eprime_condorder_zootask cve_comments
## 721 1
## 722 1
## 723 1
## 724 1
## 725 1
## 726 1
## 727 1
## 728 1
## 729 1
## 730 1
## 731 1
## 732 1
## comments_tasks
## 721 Some talking but overall compliant child. During the Zoo Task he had a hard time not catching the monkeys despite understanding the rule very well. For example, he kept saying "my bad" after catching the monkeys.
## 722 Some talking but overall compliant child. During the Zoo Task he had a hard time not catching the monkeys despite understanding the rule very well. For example, he kept saying "my bad" after catching the monkeys.
## 723 Some talking but overall compliant child. During the Zoo Task he had a hard time not catching the monkeys despite understanding the rule very well. For example, he kept saying "my bad" after catching the monkeys.
## 724 Some talking but overall compliant child. During the Zoo Task he had a hard time not catching the monkeys despite understanding the rule very well. For example, he kept saying "my bad" after catching the monkeys.
## 725 Some talking but overall compliant child. During the Zoo Task he had a hard time not catching the monkeys despite understanding the rule very well. For example, he kept saying "my bad" after catching the monkeys.
## 726 Some talking but overall compliant child. During the Zoo Task he had a hard time not catching the monkeys despite understanding the rule very well. For example, he kept saying "my bad" after catching the monkeys.
## 727 Some talking but overall compliant child. During the Zoo Task he had a hard time not catching the monkeys despite understanding the rule very well. For example, he kept saying "my bad" after catching the monkeys.
## 728 Some talking but overall compliant child. During the Zoo Task he had a hard time not catching the monkeys despite understanding the rule very well. For example, he kept saying "my bad" after catching the monkeys.
## 729 Some talking but overall compliant child. During the Zoo Task he had a hard time not catching the monkeys despite understanding the rule very well. For example, he kept saying "my bad" after catching the monkeys.
## 730 Some talking but overall compliant child. During the Zoo Task he had a hard time not catching the monkeys despite understanding the rule very well. For example, he kept saying "my bad" after catching the monkeys.
## 731 Some talking but overall compliant child. During the Zoo Task he had a hard time not catching the monkeys despite understanding the rule very well. For example, he kept saying "my bad" after catching the monkeys.
## 732 Some talking but overall compliant child. During the Zoo Task he had a hard time not catching the monkeys despite understanding the rule very well. For example, he kept saying "my bad" after catching the monkeys.
## handedness_zoo accuracy premature_responses RT_proper RT_all_go
## 721 1 99.0 NA NA NA
## 722 1 99.0 NA NA NA
## 723 1 99.0 NA NA NA
## 724 1 42.5 NA NA NA
## 725 1 42.5 NA NA NA
## 726 1 42.5 NA NA NA
## 727 1 99.0 NA NA NA
## 728 1 99.0 NA NA NA
## 729 1 99.0 NA NA NA
## 730 1 50.0 NA NA NA
## 731 1 50.0 NA NA NA
## 732 1 50.0 NA NA NALOAD and merge with trail numbers DF
# Load the file:
trialnum <- read.csv(file = '/Users/aysuerdemir/Desktop/R workspace/ERP_Zoo/Zoo_trialnumber_used_10.03.23.csv')
# convert the dataset to long format:
# id.vars argument specifies which columns in the original data frame should remain as they are without being transformed.
# measure.vars argument specifies which columns in the original data frame should be melted
# The variable.name argument specifies the name of the new column that will store the variable names from the measure.vars
# The value.name argument specifies the name of the new column that will store the values from the measure.vars.
trialnum_long <- melt(trialnum, id.vars = c("Subject"),
measure.vars = c("NeutNoGo","NeutGo",
"NegNoGo", "NegGo"),
variable.name = "StimTag",
value.name ="TrialNum")
ZOO <- merge(ZOO, trialnum_long, by=c("Subject", "StimTag"), all.x = TRUE)WRITE THE FINAL DATAFRAME INTO CSV AND SAVE TO LOCAL DRIVE:
# WRITE THE FINAL DATAFRAME INTO CSV AND SAVE TO LOCAL DRIVE:
write.csv(ZOO, "/Users/aysuerdemir/Desktop/R workspace/ERP_Zoo/CrossSectional/Mix/ZOO.csv")
head(ZOO)## Subject StimTag Emotion Condition Laterality MeanAmp_P2 MeanAmp_N2
## 1 AE050318 NegGo Neg Go Left -8.2741357 -12.4529149
## 2 AE050318 NegGo Neg Go Midline -8.6567894 -17.6405009
## 3 AE050318 NegGo Neg Go Right -10.9978696 -17.0612959
## 4 AE050318 NegNoGo Neg NoGo Right 3.0738593 -0.6563204
## 5 AE050318 NegNoGo Neg NoGo Midline 0.1368089 -2.7636031
## 6 AE050318 NegNoGo Neg NoGo Left -10.2330683 -11.7855128
## MeanAmp_P3 Latency_P2 PeakAmp_P2 Latency_N2 PeakAmp_N2 Latency_P3 PeakAmp_P3
## 1 7.006061 200 -6.982536 368 -17.043367 512 9.862223
## 2 4.369997 212 -6.242593 352 -23.058663 612 6.037603
## 3 -1.012820 208 -8.224361 352 -20.626624 576 0.844127
## 4 0.449258 232 5.512485 400 -3.018455 508 3.116096
## 5 3.345500 252 3.300071 452 -5.257907 600 6.301873
## 6 2.221935 188 -8.637317 408 -12.417423 648 5.847591
## MeanAmp_N2P2 PeakAmp_N2P2 calculator_age_cve calculator_gender_cve race
## 1 -4.178779 -10.060831 38.1 0 2
## 2 -8.983711 -16.816070 38.1 0 2
## 3 -6.063426 -12.402263 38.1 0 2
## 4 -3.730180 -8.530940 38.1 0 2
## 5 -2.900412 -8.557978 38.1 0 2
## 6 -1.552445 -3.780106 38.1 0 2
## ethnicity calculator_talkergroup_parent tso_calculated
## 1 0 1 1.9
## 2 0 1 1.9
## 3 0 1 1.9
## 4 0 1 1.9
## 5 0 1 1.9
## 6 0 1 1.9
## disfluency_sldper100words ssi_total disfluency_sldper100words_final
## 1 12 23 12
## 2 12 23 12
## 3 12 23 12
## 4 12 23 12
## 5 12 23 12
## 6 12 23 12
## talkergroup_final gfta_standard ppvt_standard evt_standard teld_rec_standard
## 1 1 121 126 123 146
## 2 1 121 126 123 146
## 3 1 121 126 123 146
## 4 1 121 126 123 146
## 5 1 121 126 123 146
## 6 1 121 126 123 146
## teld_exp_standard teld_spokenlang_standard tocs_1_total tocs_2_total
## 1 135 149 22 6
## 2 135 149 22 6
## 3 135 149 22 6
## 4 135 149 22 6
## 5 135 149 22 6
## 6 135 149 22 6
## tcs_total eprime_condorder_zootask cve_comments comments_tasks handedness_zoo
## 1 25 1 NA
## 2 25 1 NA
## 3 25 1 NA
## 4 25 1 NA
## 5 25 1 NA
## 6 25 1 NA
## accuracy premature_responses RT_proper RT_all_go TrialNum
## 1 75.83333 9.90099 774.044 706.0297 31
## 2 75.83333 9.90099 774.044 706.0297 31
## 3 75.83333 9.90099 774.044 706.0297 31
## 4 57.50000 23.52941 566.000 NaN 10
## 5 57.50000 23.52941 566.000 NaN 10
## 6 57.50000 23.52941 566.000 NaN 10REMOVE THESE KIDS BASED ON LOWER ACCURACY + NEGATIVE EEG COMMENTS:
ZOO_bad_subjects <- ZOO %>%
group_by(Subject) %>%
filter(all(accuracy[StimTag == "NeutNoGo"] <= 40) |
all(accuracy[StimTag == "NegNoGo"] <= 40) |
all(accuracy[StimTag == "NeutGo"] <= 70) |
all(accuracy[StimTag == "NegGo"] <= 70))
bad_subjects = unique(ZOO_bad_subjects$Subject)
bad_subjects## [1] "EG030618" "HH061919" "JS121321" "NL041119" "WS051018"# READ THE COMMENTS FOR THOSE LOW ACCURACY KIDS:
ZOO_filtered <- dplyr::filter(ZOO, Subject %in% bad_subjects)
ZOO_filtered <- subset(ZOO_filtered, select=c('Subject','talkergroup_final','StimTag','accuracy', 'TrialNum', 'calculator_age_cve','RT_proper', 'cve_comments','comments_tasks','handedness_zoo'))
ZOO_filtered <- ZOO_filtered %>% distinct() # Use distinct function to remove duplicates
print(ZOO_filtered)## Subject talkergroup_final StimTag accuracy TrialNum calculator_age_cve
## 1 EG030618 1 NegGo 73.94958 26 48.8
## 2 EG030618 1 NegNoGo 30.00000 6 48.8
## 3 EG030618 1 NeutGo 84.16667 46 48.8
## 4 EG030618 1 NeutNoGo 72.50000 13 48.8
## 5 HH061919 1 NegGo 80.50847 31 76.4
## 6 HH061919 1 NegNoGo 60.00000 12 76.4
## 7 HH061919 1 NeutGo 95.00000 52 76.4
## 8 HH061919 1 NeutNoGo 35.00000 11 76.4
## 9 JS121321 1 NegGo 69.49153 50 41.5
## 10 JS121321 1 NegNoGo 85.00000 22 41.5
## 11 JS121321 1 NeutGo 84.16667 57 41.5
## 12 JS121321 1 NeutNoGo 72.50000 17 41.5
## 13 NL041119 1 NegGo 80.83333 45 41.8
## 14 NL041119 1 NegNoGo 75.00000 16 41.8
## 15 NL041119 1 NeutGo 62.50000 29 41.8
## 16 NL041119 1 NeutNoGo 45.00000 9 41.8
## 17 WS051018 1 NegGo 85.00000 45 77.3
## 18 WS051018 1 NegNoGo 40.00000 8 77.3
## 19 WS051018 1 NeutGo 94.95798 46 77.3
## 20 WS051018 1 NeutNoGo 52.50000 10 77.3
## RT_proper
## 1 842.5393
## 2 894.8333
## 3 854.0990
## 4 803.7273
## 5 663.9375
## 6 666.4000
## 7 556.8509
## 8 467.3200
## 9 958.7262
## 10 1112.6667
## 11 873.1287
## 12 1088.6250
## 13 920.0206
## 14 693.3000
## 15 844.6267
## 16 891.6818
## 17 537.2451
## 18 412.8571
## 19 563.1316
## 20 491.0000
## cve_comments
## 1 No emotional regulation, no rhythm task, no reaction time
## 2 No emotional regulation, no rhythm task, no reaction time
## 3 No emotional regulation, no rhythm task, no reaction time
## 4 No emotional regulation, no rhythm task, no reaction time
## 5 Performed TELD B to see if he scored above our cut off.
## 6 Performed TELD B to see if he scored above our cut off.
## 7 Performed TELD B to see if he scored above our cut off.
## 8 Performed TELD B to see if he scored above our cut off.
## 9 Another speech sample was taken since he was borderline last time.
## 10 Another speech sample was taken since he was borderline last time.
## 11 Another speech sample was taken since he was borderline last time.
## 12 Another speech sample was taken since he was borderline last time.
## 13
## 14
## 15
## 16
## 17 The child had difficulty attending to most tasks including the NIH toolbox. Fidgeting and changing hands to response frequently. He had hard time sitting still. With the EEg cap he was restless and moving frequently as well.
## 18 The child had difficulty attending to most tasks including the NIH toolbox. Fidgeting and changing hands to response frequently. He had hard time sitting still. With the EEg cap he was restless and moving frequently as well.
## 19 The child had difficulty attending to most tasks including the NIH toolbox. Fidgeting and changing hands to response frequently. He had hard time sitting still. With the EEg cap he was restless and moving frequently as well.
## 20 The child had difficulty attending to most tasks including the NIH toolbox. Fidgeting and changing hands to response frequently. He had hard time sitting still. With the EEg cap he was restless and moving frequently as well.
## comments_tasks
## 1
## 2
## 3
## 4
## 5 During the Zoo Task, he required a lot of reminders to not push the button for the helpers. During the second block (affective) of the Zoo Task, he said he was very tired and refused to push the button for about 10-20 trials- required a lot of redirection to keep going. During the Reactivity Task, the second block(unpleasant) he moved a lot and talked a lot- required a lot of redirection to keep going and the skin conductance electrode fell off for part of that block. RESTING STATE and ZOO TASK are in one file.
## 6 During the Zoo Task, he required a lot of reminders to not push the button for the helpers. During the second block (affective) of the Zoo Task, he said he was very tired and refused to push the button for about 10-20 trials- required a lot of redirection to keep going. During the Reactivity Task, the second block(unpleasant) he moved a lot and talked a lot- required a lot of redirection to keep going and the skin conductance electrode fell off for part of that block. RESTING STATE and ZOO TASK are in one file.
## 7 During the Zoo Task, he required a lot of reminders to not push the button for the helpers. During the second block (affective) of the Zoo Task, he said he was very tired and refused to push the button for about 10-20 trials- required a lot of redirection to keep going. During the Reactivity Task, the second block(unpleasant) he moved a lot and talked a lot- required a lot of redirection to keep going and the skin conductance electrode fell off for part of that block. RESTING STATE and ZOO TASK are in one file.
## 8 During the Zoo Task, he required a lot of reminders to not push the button for the helpers. During the second block (affective) of the Zoo Task, he said he was very tired and refused to push the button for about 10-20 trials- required a lot of redirection to keep going. During the Reactivity Task, the second block(unpleasant) he moved a lot and talked a lot- required a lot of redirection to keep going and the skin conductance electrode fell off for part of that block. RESTING STATE and ZOO TASK are in one file.
## 9 It took him a while to get the task during zoo but he got it towards the 2nd quarter. Good attention in general.
## 10 It took him a while to get the task during zoo but he got it towards the 2nd quarter. Good attention in general.
## 11 It took him a while to get the task during zoo but he got it towards the 2nd quarter. Good attention in general.
## 12 It took him a while to get the task during zoo but he got it towards the 2nd quarter. Good attention in general.
## 13 DO NOT use Reactivity data. Child was rarely looking at the screen. She was talking to mom and putting her head down to rest on the table.
## 14 DO NOT use Reactivity data. Child was rarely looking at the screen. She was talking to mom and putting her head down to rest on the table.
## 15 DO NOT use Reactivity data. Child was rarely looking at the screen. She was talking to mom and putting her head down to rest on the table.
## 16 DO NOT use Reactivity data. Child was rarely looking at the screen. She was talking to mom and putting her head down to rest on the table.
## 17
## 18
## 19
## 20
## handedness_zoo
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 1
## 6 1
## 7 1
## 8 1
## 9 1
## 10 1
## 11 1
## 12 1
## 13 1
## 14 1
## 15 1
## 16 1
## 17 NA
## 18 NA
## 19 NA
## 20 NA# REMOVE THESE KIDS with <30% accuracy in Nogo, and <60% in Go.
subjects_to_remove <- c("EG030618","HH061919","WS051018")
ZOO_good <- ZOO
ZOO_good <- dplyr::filter(ZOO_good, !Subject %in% subjects_to_remove)
write.csv(ZOO_good, "/Users/aysuerdemir/Desktop/R workspace/ERP_Zoo/CrossSectional/Mix/ZOO_good.csv")DRAW N2P2 and P3 WAVEFORM GRAPHS YOURSELF
# Calculate the average of specific columns (as specificed by these electrode numbers below) across all rows and create a new column with these averages
# Combine all conditions together into a single dataframe
# This step is necessary to reverse the unclass() function we have used above.
# We need to create subsets from the original combo dataset first ADN THEN unclass before using m.measures()
combo_new <- rbind.data.frame(neg_go, neg_nogo, neut_go, neut_nogo)
combo_old <- rbind.data.frame(neg_go_old, neg_nogo_old, neut_go_old, neut_nogo_old)
# Add the "talkergroup_final" column from the "FULL" dataset to the "combo" dataset based on the common "Subject" column
combo_with_group_new <- combo_new %>%
mutate(talkergroup_final = FULL$talkergroup_final[match(Subject, FULL$Subject)])
combo_with_group_old <- combo_old %>%
mutate(talkergroup_final = FULL$talkergroup_final[match(Subject, FULL$Subject)])
# REMOVE THESE THREE KIDS FROM THE DATASET:
combo_with_group_new <- dplyr::filter(combo_with_group_new, Subject!="EG030618", Subject!="HH061919", Subject!="WS051018")
# CREATE MEAN N2P2 AND P3 VALUES FOR EACH ROW (TIMEPOINT) USING THE SPECIFIED ELECTRODE NUMBERS
newnet_rawdata <- combo_with_group_new %>%
mutate(
N2P2_waveform = rowMeans(select(., all_of(FCz_newnets))),
P3_waveform = rowMeans(select(., all_of(Pz_newnets)))
)
oldnet_rawdata <- combo_with_group_old %>%
mutate(
N2P2_waveform = rowMeans(select(., all_of(FCz_oldnets))),
P3_waveform = rowMeans(select(., all_of(Pz_oldnets)))
)
# Combine old and new net data together:
rawdata <- full_join(newnet_rawdata, oldnet_rawdata)## Joining with `by = join_by(Subject, Stimulus, Time, V1, V2, V3, V4, V5, V6, V7,
## V8, V9, V10, V11, V12, V13, V14, V15, V16, V17, V18, V19, V20, V21, V22, V23,
## V24, V25, V26, V27, V28, V29, V30, V31, V32, V33, V34, V35, V36, V37, V38, V39,
## V40, V41, V42, V43, V44, V45, V46, V47, V48, V49, V50, V51, V52, V53, V54, V55,
## V56, V57, V58, V59, V60, V61, V62, V63, V64, V65, V66, V67, V68, V69, V70, V71,
## V72, V73, V74, V75, V76, V77, V78, V79, V80, V81, V82, V83, V84, V85, V86, V87,
## V88, V89, V90, V91, V92, V93, V94, V95, V96, V97, V98, V99, V100, V101, V102,
## V103, V104, V105, V106, V107, V108, V109, V110, V111, V112, V113, V114, V115,
## V116, V117, V118, V119, V120, V121, V122, V123, V124, V125, V126, V127, V128,
## V129, talkergroup_final, N2P2_waveform, P3_waveform)`# How many kids in each group?
talkergroup_counts <- table(rawdata$talkergroup_final)
print(talkergroup_counts)/(275*4)##
## 0 1
## 46200 40700##
## 0 1
## 42 37# CREATE A SINGLE SUMMARY WAVEFORM FOR N2P2 AND P3 ACROSS ALL PARTICIPANTS IN EACH GROUP
summary_waveforms <-
rawdata %>%
group_by(Stimulus, Time, talkergroup_final) %>%
summarise(Average_N2P2 = mean(N2P2_waveform),Average_P3 = mean(P3_waveform) )## `summarise()` has grouped output by 'Stimulus', 'Time'. You can override using
## the `.groups` argument.# Define the X-axis vertical lines
vertical_lines1 <- c(180, 320, 550) # Specify the X-axis positions
# Define the X-axis vertical lines
vertical_lines2 <- c(400, 750) # Specify the X-axis positions
#Red
line_colors <- c("NeutGo" = "#D6E4F0", "NeutNoGo" = "#0000CD", "NegGo" = "#FFB6B6", "NegNoGo" = "#FF0000")
# line_colors <- c("NeutGo" = "lightskyblue1", "NeutNoGo" = "mediumblue", "NegGo" = "pink1", "NegNoGo" = "red2")
# DRAW THE GRAPHS
CWNS_N2P2_waveform <- ggplot(data = subset(summary_waveforms, talkergroup_final == 0),
aes(x = Time, y = Average_N2P2, color = Stimulus)) +
geom_line(stat = "identity", size = 1.5) +
labs(x = "Time (ms)", y = "Amplitude in Microvolts (μV)") +
scale_x_continuous(limits = c(-100, 900), breaks = seq(-100, 900, 100), position = "top") +
scale_y_continuous(limits = c(-15, 2), breaks = seq(-14, 2, 2)) +
scale_color_manual(values = line_colors, labels = c("Affective Go", "Affective NoGo","Neutral Go", "Neutral NoGo")) + # Set line colors manually
theme(
axis.text = element_text(size = 14, color = "black"),
axis.title = element_text(size = 14),
plot.background = element_rect(fill = "white"),
axis.line = element_line(linetype = "solid", color = "gray10", size = 1.4),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
legend.text = element_text(size = 12),
legend.title =element_blank()
) +
geom_vline(xintercept = vertical_lines1, color = "gray50", linetype = "dashed") +
geom_hline(yintercept = 0, color = "gray70", linetype = "dotted") +
geom_vline(xintercept = 0, color = "gray70", linetype = "dotted") +
ggtitle("CWNS N2P2 Activity") +
theme(plot.title = element_text(hjust = 0.5, size = 24, face = "bold", vjust = 2)) +
theme(legend.position = c(.85, .2)) # Set the position of the legend## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.plot(CWNS_N2P2_waveform)## Warning: Removed 96 rows containing missing values (`geom_line()`).
CWS_N2P2_waveform <- ggplot(data = subset(summary_waveforms, talkergroup_final == 1),
aes(x = Time, y = Average_N2P2, color = Stimulus)) +
geom_line(stat = "identity", size = 1.3) +
labs(x = "Time (ms)", y = "Amplitude in Microvolts (μV)") +
scale_x_continuous(limits = c(-100, 900), breaks = seq(-100, 900, 100), position = "top") +
scale_y_continuous(limits = c(-15, 2), breaks = seq(-14, 2, 2)) +
scale_color_manual(values = line_colors, labels = c("Affective Go", "Affective NoGo","Neutral Go", "Neutral NoGo")) + # Set line colors manually
theme(
axis.text = element_text(size = 14 , color = "black"),
axis.title = element_text(size = 14),
plot.background = element_rect(fill = "white"),
axis.line = element_line(linetype = "solid", color = "gray10", size = 1.4),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
legend.text = element_text(size = 12),
legend.title =element_blank()
) +
geom_vline(xintercept = vertical_lines1, color = "gray50", linetype = "dashed") +
geom_hline(yintercept = 0, color = "gray70", linetype = "dotted") +
geom_vline(xintercept = 0, color = "gray70", linetype = "dotted") +
ggtitle("CWS N2P2 Activity") +
theme(plot.title = element_text(hjust = 0.5, size = 24, face = "bold", vjust = 2)) +
theme(legend.position = c(.85, .2)) # Set the position of the legend
plot(CWS_N2P2_waveform) ## Warning: Removed 108 rows containing missing values (`geom_line()`).
CWNS_P3_waveform <- ggplot(data = subset(summary_waveforms, talkergroup_final == 0),
aes(x = Time, y = Average_P3, color = Stimulus)) +
geom_line(stat = "identity", size = 1.3) +
labs(x = "Time (ms)", y = "Amplitude in Microvolts (μV)") +
scale_x_continuous(limits = c(-100, 900), breaks = seq(-100, 900, 100), position = "top")+
scale_y_continuous(limits = c(-2, 13), breaks = seq(-2, 13, 2)) +
scale_color_manual(values = line_colors, labels = c("Affective Go", "Affective NoGo","Neutral Go", "Neutral NoGo")) + # Set line colors manually
theme(
axis.text = element_text(size = 14, color = "black"),
axis.title = element_text(size = 14),
plot.background = element_rect(fill = "white"),
axis.line = element_line(linetype = "solid", color = "gray10", size = 1.4),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
legend.text = element_text(size = 12),
legend.title =element_blank()
) +
geom_vline(xintercept = vertical_lines2, color = "gray50", linetype = "dashed") +
geom_hline(yintercept = 0, color = "gray70", linetype = "dotted") +
geom_vline(xintercept = 0, color = "gray70", linetype = "dotted") +
ggtitle("CWNS P3 Activity") +
theme(plot.title = element_text(hjust = 0.5, size = 24, face = "bold", vjust = 2)) +
theme(legend.position = c(.65, .3))
plot(CWNS_P3_waveform)## Warning: Removed 96 rows containing missing values (`geom_line()`).
CWS_P3_waveform <- ggplot(data = subset(summary_waveforms, talkergroup_final == 1),
aes(x = Time, y = Average_P3, color = Stimulus)) +
geom_line(stat = "identity", size = 1.3) +
labs(x = "Time (ms)", y = "Amplitude in Microvolts (μV)") +
scale_x_continuous(limits = c(-100, 900), breaks = seq(-100, 900, 100), position = "top")+
scale_y_continuous(limits = c(-2, 13), breaks = seq(-2, 13, 2)) +
scale_color_manual(values = line_colors, labels = c("Affective Go", "Affective NoGo","Neutral Go", "Neutral NoGo")) + # Set line colors manually
theme(
axis.text = element_text(size = 14 , color = "black"),
axis.title = element_text(size = 14),
plot.background = element_rect(fill = "white"),
axis.line = element_line(linetype = "solid", color = "gray10", size = 1.4),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
legend.text = element_text(size = 12),
legend.title =element_blank()
) +
geom_vline(xintercept = vertical_lines2, color = "gray50", linetype = "dashed") +
geom_hline(yintercept = 0, color = "gray70", linetype = "dotted") +
geom_vline(xintercept = 0, color = "gray70", linetype = "dotted") +
ggtitle("CWN P3 Activity") +
theme(plot.title = element_text(hjust = 0.5, size = 24, face = "bold", vjust = 2)) +
theme(legend.position = c(.65, .3))
plot(CWS_P3_waveform) ## Warning: Removed 96 rows containing missing values (`geom_line()`).